PETR4 Stock Prediction Using PyCaret.ipynb

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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "<img src=\"https://www.projectcubicle.com/wp-content/uploads/2022/04/Ethical-Problem-of-Artificial-Intelligence-4.jpg\"><br>\n",
    "# <center> Utilizando a inteligência artificial para prever preços de ações </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc370547",
   "metadata": {},
   "source": [
    "## Introdução<br>\n",
    "Este estudo tem o objetivo de utilizar técnicas de *machine learning* para prever o preço de fechamento das ações da Petrobrás através de técnicas de regressão linear.<br>\n",
    "A biblioteca utilizada neste estudo é a <a href=\"https://pycaret.org/\">PyCaret</a>, biblioteca *low-code* de machine learning que pode ser usada em Python para automatizar o processo de criação de modelos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fba4f3bf",
   "metadata": {},
   "source": [
    "## Importando Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6c2911dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd, yfinance as yf, plotly.express as px\n",
    "# Importando plotly.io e fazendo configurações para visualizar gráficos do plotly no GitHub\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = \"svg\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d255d67",
   "metadata": {},
   "source": [
    "## Obtendo e tratando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e335f46e",
   "metadata": {},
   "outputs": [
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       "      <th>2012-06-06</th>\n",
       "      <td>10.841078</td>\n",
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       "      <th>2022-06-01</th>\n",
       "      <td>29.900000</td>\n",
       "      <td>30.330000</td>\n",
       "      <td>29.850000</td>\n",
       "      <td>30.020000</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>2022-06-02</th>\n",
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       "      <td>30.049999</td>\n",
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       "      <td>29.760000</td>\n",
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       "      <td>30.440001</td>\n",
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       "                 Open       High        Low      Close    Volume  Dividends  \\\n",
       "Date                                                                          \n",
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       "2012-06-11  10.875222  10.920749  10.465480  10.465480  23041400        0.0   \n",
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       "2022-06-06  30.500000  30.530001  29.910000  30.299999  38952200        0.0   \n",
       "\n",
       "            Stock Splits  \n",
       "Date                      \n",
       "2012-06-06             0  \n",
       "2012-06-08             0  \n",
       "2012-06-11             0  \n",
       "2012-06-12             0  \n",
       "2012-06-13             0  \n",
       "...                  ...  \n",
       "2022-05-31             0  \n",
       "2022-06-01             0  \n",
       "2022-06-02             0  \n",
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     "execution_count": 2,
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     "output_type": "execute_result"
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   ],
   "source": [
    "# Extraindo dados das ações da Petrobrás\n",
    "df = yf.Ticker('PETR4.SA')\n",
    "# Definindo o período para análise (10 anos)\n",
    "petr4 = df.history(period='10y')\n",
    "petr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3312df7e",
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   "outputs": [
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       "      <td>30.049999</td>\n",
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       "      <td>29.760000</td>\n",
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       "      <th>2022-06-03</th>\n",
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       "      <td>30.440001</td>\n",
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       "      <td>30.280001</td>\n",
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       "      <th>2022-06-06</th>\n",
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       "      <td>30.530001</td>\n",
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       "      <td>30.299999</td>\n",
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       "                 Open       High        Low      Close    Volume\n",
       "Date                                                            \n",
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       "2012-06-08  10.841079  10.926443  10.619136  10.744334  22183800\n",
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   "source": [
    "# Removendo colunas que não serão utilizadas\n",
    "petr4 = petr4.drop(['Dividends','Stock Splits'], axis=1)\n",
    "petr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a696f64",
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       "      <td>31.27</td>\n",
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       "      <td>30.92</td>\n",
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       "      <td>29.530001</td>\n",
       "      <td>30.280001</td>\n",
       "      <td>60200400</td>\n",
       "      <td>30.41</td>\n",
       "      <td>29.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-06</th>\n",
       "      <td>30.500000</td>\n",
       "      <td>30.530001</td>\n",
       "      <td>29.910000</td>\n",
       "      <td>30.299999</td>\n",
       "      <td>38952200</td>\n",
       "      <td>30.14</td>\n",
       "      <td>29.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2480 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close    Volume   SMA7  SMA30\n",
       "Date                                                                          \n",
       "2012-06-06  10.841078  10.994731  10.789860  10.863842  28549100    NaN    NaN\n",
       "2012-06-08  10.841079  10.926443  10.619136  10.744334  22183800    NaN    NaN\n",
       "2012-06-11  10.875222  10.920749  10.465480  10.465480  23041400    NaN    NaN\n",
       "2012-06-12  10.499626  10.624824  10.425644  10.584989  24582800    NaN    NaN\n",
       "2012-06-13  10.545152  10.767095  10.471171  10.755713  27915700    NaN    NaN\n",
       "...               ...        ...        ...        ...       ...    ...    ...\n",
       "2022-05-31  30.410000  30.620001  29.809999  30.059999  83598000  31.27  29.43\n",
       "2022-06-01  29.900000  30.330000  29.850000  30.020000  46353200  30.92  29.48\n",
       "2022-06-02  29.969999  30.049999  29.549999  29.760000  50738300  30.66  29.52\n",
       "2022-06-03  29.709999  30.440001  29.530001  30.280001  60200400  30.41  29.62\n",
       "2022-06-06  30.500000  30.530001  29.910000  30.299999  38952200  30.14  29.73\n",
       "\n",
       "[2480 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adicionando Médias Móveis para acrescentar mais features ao modelo\n",
    "petr4['SMA7'] = petr4.Close.rolling(window=7).mean().round(2)\n",
    "petr4['SMA30'] = petr4.Close.rolling(window=30).mean().round(2)\n",
    "petr4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34919809",
   "metadata": {},
   "source": [
    "Observe que os dados extraídos contém o preço de abertura, o preço máximo do dia, o preço mínimo do dia, o fechamento e o volume financeiro negociado naquele determinado dia. Após criarmos as colunas que realizam o cálculo das médias móveis, temos também a média móvel dos últimos 7 e 30 dias de acordo com cada pregão analisado.<br>\n",
    "Os dados extraídos são do dia **06 de Junho de 2012** até **06 de Junho de 2022**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4fa30ae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SMA7</th>\n",
       "      <th>SMA30</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-06-01</th>\n",
       "      <td>18.882434</td>\n",
       "      <td>19.089176</td>\n",
       "      <td>18.613670</td>\n",
       "      <td>18.806629</td>\n",
       "      <td>90744900</td>\n",
       "      <td>18.25</td>\n",
       "      <td>17.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-02</th>\n",
       "      <td>18.847976</td>\n",
       "      <td>19.344157</td>\n",
       "      <td>18.785954</td>\n",
       "      <td>19.206329</td>\n",
       "      <td>103985100</td>\n",
       "      <td>18.40</td>\n",
       "      <td>17.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-04</th>\n",
       "      <td>19.337264</td>\n",
       "      <td>19.723183</td>\n",
       "      <td>19.240785</td>\n",
       "      <td>19.640486</td>\n",
       "      <td>102757300</td>\n",
       "      <td>18.66</td>\n",
       "      <td>17.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-07</th>\n",
       "      <td>19.640488</td>\n",
       "      <td>19.674945</td>\n",
       "      <td>19.302810</td>\n",
       "      <td>19.495770</td>\n",
       "      <td>74548900</td>\n",
       "      <td>18.87</td>\n",
       "      <td>17.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-08</th>\n",
       "      <td>19.413069</td>\n",
       "      <td>19.847227</td>\n",
       "      <td>19.268349</td>\n",
       "      <td>19.750748</td>\n",
       "      <td>88398600</td>\n",
       "      <td>19.15</td>\n",
       "      <td>17.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-31</th>\n",
       "      <td>30.410000</td>\n",
       "      <td>30.620001</td>\n",
       "      <td>29.809999</td>\n",
       "      <td>30.059999</td>\n",
       "      <td>83598000</td>\n",
       "      <td>31.27</td>\n",
       "      <td>29.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>29.900000</td>\n",
       "      <td>30.330000</td>\n",
       "      <td>29.850000</td>\n",
       "      <td>30.020000</td>\n",
       "      <td>46353200</td>\n",
       "      <td>30.92</td>\n",
       "      <td>29.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-02</th>\n",
       "      <td>29.969999</td>\n",
       "      <td>30.049999</td>\n",
       "      <td>29.549999</td>\n",
       "      <td>29.760000</td>\n",
       "      <td>50738300</td>\n",
       "      <td>30.66</td>\n",
       "      <td>29.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-03</th>\n",
       "      <td>29.709999</td>\n",
       "      <td>30.440001</td>\n",
       "      <td>29.530001</td>\n",
       "      <td>30.280001</td>\n",
       "      <td>60200400</td>\n",
       "      <td>30.41</td>\n",
       "      <td>29.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-06</th>\n",
       "      <td>30.500000</td>\n",
       "      <td>30.530001</td>\n",
       "      <td>29.910000</td>\n",
       "      <td>30.299999</td>\n",
       "      <td>38952200</td>\n",
       "      <td>30.14</td>\n",
       "      <td>29.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close     Volume   SMA7  \\\n",
       "Date                                                                       \n",
       "2021-06-01  18.882434  19.089176  18.613670  18.806629   90744900  18.25   \n",
       "2021-06-02  18.847976  19.344157  18.785954  19.206329  103985100  18.40   \n",
       "2021-06-04  19.337264  19.723183  19.240785  19.640486  102757300  18.66   \n",
       "2021-06-07  19.640488  19.674945  19.302810  19.495770   74548900  18.87   \n",
       "2021-06-08  19.413069  19.847227  19.268349  19.750748   88398600  19.15   \n",
       "...               ...        ...        ...        ...        ...    ...   \n",
       "2022-05-31  30.410000  30.620001  29.809999  30.059999   83598000  31.27   \n",
       "2022-06-01  29.900000  30.330000  29.850000  30.020000   46353200  30.92   \n",
       "2022-06-02  29.969999  30.049999  29.549999  29.760000   50738300  30.66   \n",
       "2022-06-03  29.709999  30.440001  29.530001  30.280001   60200400  30.41   \n",
       "2022-06-06  30.500000  30.530001  29.910000  30.299999   38952200  30.14   \n",
       "\n",
       "            SMA30  \n",
       "Date               \n",
       "2021-06-01  17.22  \n",
       "2021-06-02  17.31  \n",
       "2021-06-04  17.42  \n",
       "2021-06-07  17.53  \n",
       "2021-06-08  17.64  \n",
       "...           ...  \n",
       "2022-05-31  29.43  \n",
       "2022-06-01  29.48  \n",
       "2022-06-02  29.52  \n",
       "2022-06-03  29.62  \n",
       "2022-06-06  29.73  \n",
       "\n",
       "[253 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retirando os últimos 253 dias de pregão (cerca de 1 ano) para testar a previsão\n",
    "petr4_predict = petr4.tail(253)\n",
    "petr4_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d22b01",
   "metadata": {},
   "source": [
    "Criamos um novo *dataframe* contendo os últimos 253 dias de pregão (de 01 de Junho de 2021 até 06 de Junho de 2022) para fazermos um teste do nosso modelo de previsão posteriormente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f8dd81ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SMA7</th>\n",
       "      <th>SMA30</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-06-06</th>\n",
       "      <td>10.841078</td>\n",
       "      <td>10.994731</td>\n",
       "      <td>10.789860</td>\n",
       "      <td>10.863842</td>\n",
       "      <td>28549100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-08</th>\n",
       "      <td>10.841079</td>\n",
       "      <td>10.926443</td>\n",
       "      <td>10.619136</td>\n",
       "      <td>10.744334</td>\n",
       "      <td>22183800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-11</th>\n",
       "      <td>10.875222</td>\n",
       "      <td>10.920749</td>\n",
       "      <td>10.465480</td>\n",
       "      <td>10.465480</td>\n",
       "      <td>23041400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-12</th>\n",
       "      <td>10.499626</td>\n",
       "      <td>10.624824</td>\n",
       "      <td>10.425644</td>\n",
       "      <td>10.584989</td>\n",
       "      <td>24582800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-13</th>\n",
       "      <td>10.545152</td>\n",
       "      <td>10.767095</td>\n",
       "      <td>10.471171</td>\n",
       "      <td>10.755713</td>\n",
       "      <td>27915700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-25</th>\n",
       "      <td>18.248424</td>\n",
       "      <td>18.344905</td>\n",
       "      <td>17.766027</td>\n",
       "      <td>17.807375</td>\n",
       "      <td>63672000</td>\n",
       "      <td>18.04</td>\n",
       "      <td>16.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-26</th>\n",
       "      <td>17.855615</td>\n",
       "      <td>18.041683</td>\n",
       "      <td>17.676438</td>\n",
       "      <td>17.979660</td>\n",
       "      <td>46646900</td>\n",
       "      <td>17.99</td>\n",
       "      <td>16.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-27</th>\n",
       "      <td>17.972768</td>\n",
       "      <td>18.089921</td>\n",
       "      <td>17.759135</td>\n",
       "      <td>17.855614</td>\n",
       "      <td>48877800</td>\n",
       "      <td>17.94</td>\n",
       "      <td>16.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-28</th>\n",
       "      <td>18.055466</td>\n",
       "      <td>18.620561</td>\n",
       "      <td>18.021008</td>\n",
       "      <td>18.599886</td>\n",
       "      <td>129526400</td>\n",
       "      <td>18.03</td>\n",
       "      <td>17.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>18.579212</td>\n",
       "      <td>18.744607</td>\n",
       "      <td>18.455168</td>\n",
       "      <td>18.517191</td>\n",
       "      <td>46298900</td>\n",
       "      <td>18.12</td>\n",
       "      <td>17.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2227 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close     Volume   SMA7  \\\n",
       "Date                                                                       \n",
       "2012-06-06  10.841078  10.994731  10.789860  10.863842   28549100    NaN   \n",
       "2012-06-08  10.841079  10.926443  10.619136  10.744334   22183800    NaN   \n",
       "2012-06-11  10.875222  10.920749  10.465480  10.465480   23041400    NaN   \n",
       "2012-06-12  10.499626  10.624824  10.425644  10.584989   24582800    NaN   \n",
       "2012-06-13  10.545152  10.767095  10.471171  10.755713   27915700    NaN   \n",
       "...               ...        ...        ...        ...        ...    ...   \n",
       "2021-05-25  18.248424  18.344905  17.766027  17.807375   63672000  18.04   \n",
       "2021-05-26  17.855615  18.041683  17.676438  17.979660   46646900  17.99   \n",
       "2021-05-27  17.972768  18.089921  17.759135  17.855614   48877800  17.94   \n",
       "2021-05-28  18.055466  18.620561  18.021008  18.599886  129526400  18.03   \n",
       "2021-05-31  18.579212  18.744607  18.455168  18.517191   46298900  18.12   \n",
       "\n",
       "            SMA30  \n",
       "Date               \n",
       "2012-06-06    NaN  \n",
       "2012-06-08    NaN  \n",
       "2012-06-11    NaN  \n",
       "2012-06-12    NaN  \n",
       "2012-06-13    NaN  \n",
       "...           ...  \n",
       "2021-05-25  16.85  \n",
       "2021-05-26  16.92  \n",
       "2021-05-27  16.97  \n",
       "2021-05-28  17.06  \n",
       "2021-05-31  17.15  \n",
       "\n",
       "[2227 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removendo os últimos 253 dias do dataframe original\n",
    "petr4.drop(petr4.tail(253).index, inplace=True)\n",
    "petr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e65a41e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
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       "      <th>SMA7</th>\n",
       "      <th>SMA30</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-07-19</th>\n",
       "      <td>11.000421</td>\n",
       "      <td>11.250819</td>\n",
       "      <td>11.000421</td>\n",
       "      <td>11.165457</td>\n",
       "      <td>20781700</td>\n",
       "      <td>10.93</td>\n",
       "      <td>10.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-20</th>\n",
       "      <td>11.028874</td>\n",
       "      <td>11.131309</td>\n",
       "      <td>10.875221</td>\n",
       "      <td>10.915057</td>\n",
       "      <td>19025100</td>\n",
       "      <td>10.98</td>\n",
       "      <td>10.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-23</th>\n",
       "      <td>10.738643</td>\n",
       "      <td>10.835388</td>\n",
       "      <td>10.476863</td>\n",
       "      <td>10.789860</td>\n",
       "      <td>21365500</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-24</th>\n",
       "      <td>10.841077</td>\n",
       "      <td>10.926441</td>\n",
       "      <td>10.602062</td>\n",
       "      <td>10.693116</td>\n",
       "      <td>19547500</td>\n",
       "      <td>10.95</td>\n",
       "      <td>10.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07-25</th>\n",
       "      <td>10.812622</td>\n",
       "      <td>10.932129</td>\n",
       "      <td>10.670351</td>\n",
       "      <td>10.812622</td>\n",
       "      <td>17749100</td>\n",
       "      <td>10.90</td>\n",
       "      <td>10.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-25</th>\n",
       "      <td>18.248424</td>\n",
       "      <td>18.344905</td>\n",
       "      <td>17.766027</td>\n",
       "      <td>17.807375</td>\n",
       "      <td>63672000</td>\n",
       "      <td>18.04</td>\n",
       "      <td>16.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-26</th>\n",
       "      <td>17.855615</td>\n",
       "      <td>18.041683</td>\n",
       "      <td>17.676438</td>\n",
       "      <td>17.979660</td>\n",
       "      <td>46646900</td>\n",
       "      <td>17.99</td>\n",
       "      <td>16.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-27</th>\n",
       "      <td>17.972768</td>\n",
       "      <td>18.089921</td>\n",
       "      <td>17.759135</td>\n",
       "      <td>17.855614</td>\n",
       "      <td>48877800</td>\n",
       "      <td>17.94</td>\n",
       "      <td>16.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-28</th>\n",
       "      <td>18.055466</td>\n",
       "      <td>18.620561</td>\n",
       "      <td>18.021008</td>\n",
       "      <td>18.599886</td>\n",
       "      <td>129526400</td>\n",
       "      <td>18.03</td>\n",
       "      <td>17.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>18.579212</td>\n",
       "      <td>18.744607</td>\n",
       "      <td>18.455168</td>\n",
       "      <td>18.517191</td>\n",
       "      <td>46298900</td>\n",
       "      <td>18.12</td>\n",
       "      <td>17.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2198 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close     Volume   SMA7  \\\n",
       "Date                                                                       \n",
       "2012-07-19  11.000421  11.250819  11.000421  11.165457   20781700  10.93   \n",
       "2012-07-20  11.028874  11.131309  10.875221  10.915057   19025100  10.98   \n",
       "2012-07-23  10.738643  10.835388  10.476863  10.789860   21365500  11.01   \n",
       "2012-07-24  10.841077  10.926441  10.602062  10.693116   19547500  10.95   \n",
       "2012-07-25  10.812622  10.932129  10.670351  10.812622   17749100  10.90   \n",
       "...               ...        ...        ...        ...        ...    ...   \n",
       "2021-05-25  18.248424  18.344905  17.766027  17.807375   63672000  18.04   \n",
       "2021-05-26  17.855615  18.041683  17.676438  17.979660   46646900  17.99   \n",
       "2021-05-27  17.972768  18.089921  17.759135  17.855614   48877800  17.94   \n",
       "2021-05-28  18.055466  18.620561  18.021008  18.599886  129526400  18.03   \n",
       "2021-05-31  18.579212  18.744607  18.455168  18.517191   46298900  18.12   \n",
       "\n",
       "            SMA30  \n",
       "Date               \n",
       "2012-07-19  10.71  \n",
       "2012-07-20  10.72  \n",
       "2012-07-23  10.72  \n",
       "2012-07-24  10.72  \n",
       "2012-07-25  10.73  \n",
       "...           ...  \n",
       "2021-05-25  16.85  \n",
       "2021-05-26  16.92  \n",
       "2021-05-27  16.97  \n",
       "2021-05-28  17.06  \n",
       "2021-05-31  17.15  \n",
       "\n",
       "[2198 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removendo dados nulos\n",
    "petr4.dropna(inplace=True)\n",
    "petr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3dda1959",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removendo índices\n",
    "petr4.reset_index(drop=True, inplace=True)\n",
    "petr4_predict.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3b60167f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SMA7</th>\n",
       "      <th>SMA30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.000421</td>\n",
       "      <td>11.250819</td>\n",
       "      <td>11.000421</td>\n",
       "      <td>11.165457</td>\n",
       "      <td>20781700</td>\n",
       "      <td>10.93</td>\n",
       "      <td>10.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.028874</td>\n",
       "      <td>11.131309</td>\n",
       "      <td>10.875221</td>\n",
       "      <td>10.915057</td>\n",
       "      <td>19025100</td>\n",
       "      <td>10.98</td>\n",
       "      <td>10.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.738643</td>\n",
       "      <td>10.835388</td>\n",
       "      <td>10.476863</td>\n",
       "      <td>10.789860</td>\n",
       "      <td>21365500</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.841077</td>\n",
       "      <td>10.926441</td>\n",
       "      <td>10.602062</td>\n",
       "      <td>10.693116</td>\n",
       "      <td>19547500</td>\n",
       "      <td>10.95</td>\n",
       "      <td>10.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.812622</td>\n",
       "      <td>10.932129</td>\n",
       "      <td>10.670351</td>\n",
       "      <td>10.812622</td>\n",
       "      <td>17749100</td>\n",
       "      <td>10.90</td>\n",
       "      <td>10.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2193</th>\n",
       "      <td>18.248424</td>\n",
       "      <td>18.344905</td>\n",
       "      <td>17.766027</td>\n",
       "      <td>17.807375</td>\n",
       "      <td>63672000</td>\n",
       "      <td>18.04</td>\n",
       "      <td>16.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2194</th>\n",
       "      <td>17.855615</td>\n",
       "      <td>18.041683</td>\n",
       "      <td>17.676438</td>\n",
       "      <td>17.979660</td>\n",
       "      <td>46646900</td>\n",
       "      <td>17.99</td>\n",
       "      <td>16.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>17.972768</td>\n",
       "      <td>18.089921</td>\n",
       "      <td>17.759135</td>\n",
       "      <td>17.855614</td>\n",
       "      <td>48877800</td>\n",
       "      <td>17.94</td>\n",
       "      <td>16.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2196</th>\n",
       "      <td>18.055466</td>\n",
       "      <td>18.620561</td>\n",
       "      <td>18.021008</td>\n",
       "      <td>18.599886</td>\n",
       "      <td>129526400</td>\n",
       "      <td>18.03</td>\n",
       "      <td>17.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2197</th>\n",
       "      <td>18.579212</td>\n",
       "      <td>18.744607</td>\n",
       "      <td>18.455168</td>\n",
       "      <td>18.517191</td>\n",
       "      <td>46298900</td>\n",
       "      <td>18.12</td>\n",
       "      <td>17.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2198 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Open       High        Low      Close     Volume   SMA7  SMA30\n",
       "0     11.000421  11.250819  11.000421  11.165457   20781700  10.93  10.71\n",
       "1     11.028874  11.131309  10.875221  10.915057   19025100  10.98  10.72\n",
       "2     10.738643  10.835388  10.476863  10.789860   21365500  11.01  10.72\n",
       "3     10.841077  10.926441  10.602062  10.693116   19547500  10.95  10.72\n",
       "4     10.812622  10.932129  10.670351  10.812622   17749100  10.90  10.73\n",
       "...         ...        ...        ...        ...        ...    ...    ...\n",
       "2193  18.248424  18.344905  17.766027  17.807375   63672000  18.04  16.85\n",
       "2194  17.855615  18.041683  17.676438  17.979660   46646900  17.99  16.92\n",
       "2195  17.972768  18.089921  17.759135  17.855614   48877800  17.94  16.97\n",
       "2196  18.055466  18.620561  18.021008  18.599886  129526400  18.03  17.06\n",
       "2197  18.579212  18.744607  18.455168  18.517191   46298900  18.12  17.15\n",
       "\n",
       "[2198 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando dataframe\n",
    "petr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9be5e0e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SMA7</th>\n",
       "      <th>SMA30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Open</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999198</td>\n",
       "      <td>0.998981</td>\n",
       "      <td>0.998116</td>\n",
       "      <td>0.086853</td>\n",
       "      <td>0.995113</td>\n",
       "      <td>0.963394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>0.999198</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998686</td>\n",
       "      <td>0.999015</td>\n",
       "      <td>0.100695</td>\n",
       "      <td>0.994921</td>\n",
       "      <td>0.964153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>0.998981</td>\n",
       "      <td>0.998686</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999137</td>\n",
       "      <td>0.070257</td>\n",
       "      <td>0.993589</td>\n",
       "      <td>0.959956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Close</th>\n",
       "      <td>0.998116</td>\n",
       "      <td>0.999015</td>\n",
       "      <td>0.999137</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.084603</td>\n",
       "      <td>0.993487</td>\n",
       "      <td>0.961030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volume</th>\n",
       "      <td>0.086853</td>\n",
       "      <td>0.100695</td>\n",
       "      <td>0.070257</td>\n",
       "      <td>0.084603</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.102619</td>\n",
       "      <td>0.135411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMA7</th>\n",
       "      <td>0.995113</td>\n",
       "      <td>0.994921</td>\n",
       "      <td>0.993589</td>\n",
       "      <td>0.993487</td>\n",
       "      <td>0.102619</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.975141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMA30</th>\n",
       "      <td>0.963394</td>\n",
       "      <td>0.964153</td>\n",
       "      <td>0.959956</td>\n",
       "      <td>0.961030</td>\n",
       "      <td>0.135411</td>\n",
       "      <td>0.975141</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Open      High       Low     Close    Volume      SMA7     SMA30\n",
       "Open    1.000000  0.999198  0.998981  0.998116  0.086853  0.995113  0.963394\n",
       "High    0.999198  1.000000  0.998686  0.999015  0.100695  0.994921  0.964153\n",
       "Low     0.998981  0.998686  1.000000  0.999137  0.070257  0.993589  0.959956\n",
       "Close   0.998116  0.999015  0.999137  1.000000  0.084603  0.993487  0.961030\n",
       "Volume  0.086853  0.100695  0.070257  0.084603  1.000000  0.102619  0.135411\n",
       "SMA7    0.995113  0.994921  0.993589  0.993487  0.102619  1.000000  0.975141\n",
       "SMA30   0.963394  0.964153  0.959956  0.961030  0.135411  0.975141  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando correlações entre as variáveis\n",
    "petr4.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be5cdc2f",
   "metadata": {},
   "source": [
    "Analisando as correlações acima, podemos perceber que os preços de abertura, máximo, mínimo, fechamento e as médias móveis apresentam fortes correlações entre si."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b23338",
   "metadata": {},
   "source": [
    "## Utilizando o PyCaret para criar um modelo de Machine Learning para prever os preços de fechamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f29346d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th id=\"T_0ced1_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_0ced1_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
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       "      <th id=\"T_0ced1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_0ced1_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
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       "      <td id=\"T_0ced1_row1_col1\" class=\"data row1 col1\" >Close</td>\n",
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       "      <td id=\"T_0ced1_row2_col0\" class=\"data row2 col0\" >Original Data</td>\n",
       "      <td id=\"T_0ced1_row2_col1\" class=\"data row2 col1\" >(2198, 7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_0ced1_row3_col0\" class=\"data row3 col0\" >Missing Values</td>\n",
       "      <td id=\"T_0ced1_row3_col1\" class=\"data row3 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_0ced1_row4_col0\" class=\"data row4 col0\" >Numeric Features</td>\n",
       "      <td id=\"T_0ced1_row4_col1\" class=\"data row4 col1\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_0ced1_row5_col0\" class=\"data row5 col0\" >Categorical Features</td>\n",
       "      <td id=\"T_0ced1_row5_col1\" class=\"data row5 col1\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_0ced1_row6_col0\" class=\"data row6 col0\" >Ordinal Features</td>\n",
       "      <td id=\"T_0ced1_row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_0ced1_row7_col0\" class=\"data row7 col0\" >High Cardinality Features</td>\n",
       "      <td id=\"T_0ced1_row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_0ced1_row8_col0\" class=\"data row8 col0\" >High Cardinality Method</td>\n",
       "      <td id=\"T_0ced1_row8_col1\" class=\"data row8 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_0ced1_row9_col0\" class=\"data row9 col0\" >Transformed Train Set</td>\n",
       "      <td id=\"T_0ced1_row9_col1\" class=\"data row9 col1\" >(1538, 6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_0ced1_row10_col0\" class=\"data row10 col0\" >Transformed Test Set</td>\n",
       "      <td id=\"T_0ced1_row10_col1\" class=\"data row10 col1\" >(660, 6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_0ced1_row11_col0\" class=\"data row11 col0\" >Shuffle Train-Test</td>\n",
       "      <td id=\"T_0ced1_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_0ced1_row12_col0\" class=\"data row12 col0\" >Stratify Train-Test</td>\n",
       "      <td id=\"T_0ced1_row12_col1\" class=\"data row12 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_0ced1_row13_col0\" class=\"data row13 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_0ced1_row13_col1\" class=\"data row13 col1\" >KFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_0ced1_row14_col0\" class=\"data row14 col0\" >Fold Number</td>\n",
       "      <td id=\"T_0ced1_row14_col1\" class=\"data row14 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_0ced1_row15_col0\" class=\"data row15 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_0ced1_row15_col1\" class=\"data row15 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_0ced1_row16_col0\" class=\"data row16 col0\" >Use GPU</td>\n",
       "      <td id=\"T_0ced1_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_0ced1_row17_col0\" class=\"data row17 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_0ced1_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_0ced1_row18_col0\" class=\"data row18 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_0ced1_row18_col1\" class=\"data row18 col1\" >reg-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_0ced1_row19_col0\" class=\"data row19 col0\" >USI</td>\n",
       "      <td id=\"T_0ced1_row19_col1\" class=\"data row19 col1\" >eb1e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_0ced1_row20_col0\" class=\"data row20 col0\" >Imputation Type</td>\n",
       "      <td id=\"T_0ced1_row20_col1\" class=\"data row20 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_0ced1_row21_col0\" class=\"data row21 col0\" >Iterative Imputation Iteration</td>\n",
       "      <td id=\"T_0ced1_row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_0ced1_row22_col0\" class=\"data row22 col0\" >Numeric Imputer</td>\n",
       "      <td id=\"T_0ced1_row22_col1\" class=\"data row22 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_0ced1_row23_col0\" class=\"data row23 col0\" >Iterative Imputation Numeric Model</td>\n",
       "      <td id=\"T_0ced1_row23_col1\" class=\"data row23 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_0ced1_row24_col0\" class=\"data row24 col0\" >Categorical Imputer</td>\n",
       "      <td id=\"T_0ced1_row24_col1\" class=\"data row24 col1\" >constant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_0ced1_row25_col0\" class=\"data row25 col0\" >Iterative Imputation Categorical Model</td>\n",
       "      <td id=\"T_0ced1_row25_col1\" class=\"data row25 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_0ced1_row26_col0\" class=\"data row26 col0\" >Unknown Categoricals Handling</td>\n",
       "      <td id=\"T_0ced1_row26_col1\" class=\"data row26 col1\" >least_frequent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_0ced1_row27_col0\" class=\"data row27 col0\" >Normalize</td>\n",
       "      <td id=\"T_0ced1_row27_col1\" class=\"data row27 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_0ced1_row28_col0\" class=\"data row28 col0\" >Normalize Method</td>\n",
       "      <td id=\"T_0ced1_row28_col1\" class=\"data row28 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_0ced1_row29_col0\" class=\"data row29 col0\" >Transformation</td>\n",
       "      <td id=\"T_0ced1_row29_col1\" class=\"data row29 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_0ced1_row30_col0\" class=\"data row30 col0\" >Transformation Method</td>\n",
       "      <td id=\"T_0ced1_row30_col1\" class=\"data row30 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_0ced1_row31_col0\" class=\"data row31 col0\" >PCA</td>\n",
       "      <td id=\"T_0ced1_row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "      <td id=\"T_0ced1_row32_col0\" class=\"data row32 col0\" >PCA Method</td>\n",
       "      <td id=\"T_0ced1_row32_col1\" class=\"data row32 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "      <td id=\"T_0ced1_row33_col0\" class=\"data row33 col0\" >PCA Components</td>\n",
       "      <td id=\"T_0ced1_row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "      <td id=\"T_0ced1_row34_col0\" class=\"data row34 col0\" >Ignore Low Variance</td>\n",
       "      <td id=\"T_0ced1_row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "      <td id=\"T_0ced1_row35_col0\" class=\"data row35 col0\" >Combine Rare Levels</td>\n",
       "      <td id=\"T_0ced1_row35_col1\" class=\"data row35 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "      <td id=\"T_0ced1_row36_col0\" class=\"data row36 col0\" >Rare Level Threshold</td>\n",
       "      <td id=\"T_0ced1_row36_col1\" class=\"data row36 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "      <td id=\"T_0ced1_row37_col0\" class=\"data row37 col0\" >Numeric Binning</td>\n",
       "      <td id=\"T_0ced1_row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "      <td id=\"T_0ced1_row38_col0\" class=\"data row38 col0\" >Remove Outliers</td>\n",
       "      <td id=\"T_0ced1_row38_col1\" class=\"data row38 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "      <td id=\"T_0ced1_row39_col0\" class=\"data row39 col0\" >Outliers Threshold</td>\n",
       "      <td id=\"T_0ced1_row39_col1\" class=\"data row39 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "      <td id=\"T_0ced1_row40_col0\" class=\"data row40 col0\" >Remove Multicollinearity</td>\n",
       "      <td id=\"T_0ced1_row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "      <td id=\"T_0ced1_row41_col0\" class=\"data row41 col0\" >Multicollinearity Threshold</td>\n",
       "      <td id=\"T_0ced1_row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "      <td id=\"T_0ced1_row42_col0\" class=\"data row42 col0\" >Remove Perfect Collinearity</td>\n",
       "      <td id=\"T_0ced1_row42_col1\" class=\"data row42 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "      <td id=\"T_0ced1_row43_col0\" class=\"data row43 col0\" >Clustering</td>\n",
       "      <td id=\"T_0ced1_row43_col1\" class=\"data row43 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "      <td id=\"T_0ced1_row44_col0\" class=\"data row44 col0\" >Clustering Iteration</td>\n",
       "      <td id=\"T_0ced1_row44_col1\" class=\"data row44 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "      <td id=\"T_0ced1_row45_col0\" class=\"data row45 col0\" >Polynomial Features</td>\n",
       "      <td id=\"T_0ced1_row45_col1\" class=\"data row45 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "      <td id=\"T_0ced1_row46_col0\" class=\"data row46 col0\" >Polynomial Degree</td>\n",
       "      <td id=\"T_0ced1_row46_col1\" class=\"data row46 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "      <td id=\"T_0ced1_row47_col0\" class=\"data row47 col0\" >Trignometry Features</td>\n",
       "      <td id=\"T_0ced1_row47_col1\" class=\"data row47 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "      <td id=\"T_0ced1_row48_col0\" class=\"data row48 col0\" >Polynomial Threshold</td>\n",
       "      <td id=\"T_0ced1_row48_col1\" class=\"data row48 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "      <td id=\"T_0ced1_row49_col0\" class=\"data row49 col0\" >Group Features</td>\n",
       "      <td id=\"T_0ced1_row49_col1\" class=\"data row49 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "      <td id=\"T_0ced1_row50_col0\" class=\"data row50 col0\" >Feature Selection</td>\n",
       "      <td id=\"T_0ced1_row50_col1\" class=\"data row50 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "      <td id=\"T_0ced1_row51_col0\" class=\"data row51 col0\" >Feature Selection Method</td>\n",
       "      <td id=\"T_0ced1_row51_col1\" class=\"data row51 col1\" >classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "      <td id=\"T_0ced1_row52_col0\" class=\"data row52 col0\" >Features Selection Threshold</td>\n",
       "      <td id=\"T_0ced1_row52_col1\" class=\"data row52 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "      <td id=\"T_0ced1_row53_col0\" class=\"data row53 col0\" >Feature Interaction</td>\n",
       "      <td id=\"T_0ced1_row53_col1\" class=\"data row53 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "      <td id=\"T_0ced1_row54_col0\" class=\"data row54 col0\" >Feature Ratio</td>\n",
       "      <td id=\"T_0ced1_row54_col1\" class=\"data row54 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "      <td id=\"T_0ced1_row55_col0\" class=\"data row55 col0\" >Interaction Threshold</td>\n",
       "      <td id=\"T_0ced1_row55_col1\" class=\"data row55 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "      <td id=\"T_0ced1_row56_col0\" class=\"data row56 col0\" >Transform Target</td>\n",
       "      <td id=\"T_0ced1_row56_col1\" class=\"data row56 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0ced1_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "      <td id=\"T_0ced1_row57_col0\" class=\"data row57 col0\" >Transform Target Method</td>\n",
       "      <td id=\"T_0ced1_row57_col1\" class=\"data row57 col1\" >box-cox</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f420c1fc70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "(False,\n",
       " {'mae': <pycaret.containers.metrics.regression.MAEMetricContainer at 0x1f420c1fbb0>,\n",
       "  'mse': <pycaret.containers.metrics.regression.MSEMetricContainer at 0x1f420c1fdf0>,\n",
       "  'rmse': <pycaret.containers.metrics.regression.RMSEMetricContainer at 0x1f420c1ff70>,\n",
       "  'r2': <pycaret.containers.metrics.regression.R2MetricContainer at 0x1f420c1fe20>,\n",
       "  'rmsle': <pycaret.containers.metrics.regression.RMSLEMetricContainer at 0x1f420c1fa60>,\n",
       "  'mape': <pycaret.containers.metrics.regression.MAPEMetricContainer at 0x1f420c1ffa0>},\n",
       " 0       11.165457\n",
       " 1       10.915057\n",
       " 2       10.789860\n",
       " 3       10.693116\n",
       " 4       10.812622\n",
       "           ...    \n",
       " 2193    17.807375\n",
       " 2194    17.979660\n",
       " 2195    17.855614\n",
       " 2196    18.599886\n",
       " 2197    18.517191\n",
       " Name: Close, Length: 2198, dtype: float32,\n",
       " 5,\n",
       " [],\n",
       " -1,\n",
       " None,\n",
       " 'reg-default-name',\n",
       " False,\n",
       "            Open       High        Low       Volume       SMA7      SMA30\n",
       " 0     11.000422  11.250819  11.000422   20781700.0  10.930000  10.710000\n",
       " 1     11.028873  11.131310  10.875221   19025100.0  10.980000  10.720000\n",
       " 2     10.738643  10.835387  10.476863   21365500.0  11.010000  10.720000\n",
       " 3     10.841078  10.926441  10.602061   19547500.0  10.950000  10.720000\n",
       " 4     10.812622  10.932130  10.670351   17749100.0  10.900000  10.730000\n",
       " ...         ...        ...        ...          ...        ...        ...\n",
       " 2193  18.248425  18.344904  17.766027   63672000.0  18.040001  16.850000\n",
       " 2194  17.855614  18.041683  17.676437   46646900.0  17.990000  16.920000\n",
       " 2195  17.972767  18.089922  17.759134   48877800.0  17.940001  16.969999\n",
       " 2196  18.055466  18.620560  18.021008  129526400.0  18.030001  17.059999\n",
       " 2197  18.579212  18.744608  18.455168   46298900.0  18.120001  17.150000\n",
       " \n",
       " [2198 rows x 6 columns],\n",
       " 809      4.700606\n",
       " 403      7.546515\n",
       " 304     10.527999\n",
       " 1743    17.006662\n",
       " 866      2.727588\n",
       "           ...    \n",
       " 883      2.758513\n",
       " 319     11.769318\n",
       " 899      4.576906\n",
       " 2102    20.574409\n",
       " 1029     8.139468\n",
       " Name: Close, Length: 660, dtype: float32,\n",
       " [<pandas.io.formats.style.Styler at 0x1f420c1fc70>],\n",
       "            Open       High        Low      Volume       SMA7  SMA30\n",
       " 210   11.554696  11.641705  11.485089  24961000.0  11.600000  11.34\n",
       " 489   11.967984  11.992724  11.800989  27591400.0  11.370000  11.06\n",
       " 297   10.748422  10.841230  10.655613  15455100.0  10.840000  10.48\n",
       " 1371  11.392784  12.141169  11.392784  89768200.0  11.360000  10.32\n",
       " 1642  17.511278  17.582253  17.182217  41978700.0  17.389999  16.82\n",
       " ...         ...        ...        ...         ...        ...    ...\n",
       " 1147   9.104330   9.203290   8.807449  42454600.0   9.200000   9.46\n",
       " 2154  15.560814  15.887498  15.520813  50099700.0  15.580000  15.76\n",
       " 1766  16.676529  16.781044  16.441372  54958200.0  16.180000  16.65\n",
       " 1122   9.438319   9.531094   9.221844  35977000.0   9.570000   9.44\n",
       " 1346   9.388840   9.432135   9.314619  22639700.0   9.400000   9.71\n",
       " \n",
       " [1538 rows x 6 columns],\n",
       " 123,\n",
       " {'lr': <pycaret.containers.models.regression.LinearRegressionContainer at 0x1f420c31df0>,\n",
       "  'lasso': <pycaret.containers.models.regression.LassoRegressionContainer at 0x1f420c31520>,\n",
       "  'ridge': <pycaret.containers.models.regression.RidgeRegressionContainer at 0x1f420c5b340>,\n",
       "  'en': <pycaret.containers.models.regression.ElasticNetContainer at 0x1f420c5bd90>,\n",
       "  'lar': <pycaret.containers.models.regression.LarsContainer at 0x1f420c5bb20>,\n",
       "  'llar': <pycaret.containers.models.regression.LassoLarsContainer at 0x1f420c5b700>,\n",
       "  'omp': <pycaret.containers.models.regression.OrthogonalMatchingPursuitContainer at 0x1f420c5b8b0>,\n",
       "  'br': <pycaret.containers.models.regression.BayesianRidgeContainer at 0x1f420c5b4f0>,\n",
       "  'ard': <pycaret.containers.models.regression.AutomaticRelevanceDeterminationContainer at 0x1f420c5b730>,\n",
       "  'par': <pycaret.containers.models.regression.PassiveAggressiveRegressorContainer at 0x1f420c77a60>,\n",
       "  'ransac': <pycaret.containers.models.regression.RANSACRegressorContainer at 0x1f420c77460>,\n",
       "  'tr': <pycaret.containers.models.regression.TheilSenRegressorContainer at 0x1f420c77a90>,\n",
       "  'huber': <pycaret.containers.models.regression.HuberRegressorContainer at 0x1f420c778b0>,\n",
       "  'kr': <pycaret.containers.models.regression.KernelRidgeContainer at 0x1f420c77b20>,\n",
       "  'svm': <pycaret.containers.models.regression.SVRContainer at 0x1f420c77f70>,\n",
       "  'knn': <pycaret.containers.models.regression.KNeighborsRegressorContainer at 0x1f420c77dc0>,\n",
       "  'dt': <pycaret.containers.models.regression.DecisionTreeRegressorContainer at 0x1f420c77040>,\n",
       "  'rf': <pycaret.containers.models.regression.RandomForestRegressorContainer at 0x1f420cc1160>,\n",
       "  'et': <pycaret.containers.models.regression.ExtraTreesRegressorContainer at 0x1f420cc18e0>,\n",
       "  'ada': <pycaret.containers.models.regression.AdaBoostRegressorContainer at 0x1f420cc15b0>,\n",
       "  'gbr': <pycaret.containers.models.regression.GradientBoostingRegressorContainer at 0x1f420cc1730>,\n",
       "  'mlp': <pycaret.containers.models.regression.MLPRegressorContainer at 0x1f420c3d670>,\n",
       "  'xgboost': <pycaret.containers.models.regression.XGBRegressorContainer at 0x1f420c3d400>,\n",
       "  'lightgbm': <pycaret.containers.models.regression.LGBMRegressorContainer at 0x1f420c3d7c0>,\n",
       "  'catboost': <pycaret.containers.models.regression.CatBoostRegressorContainer at 0x1f420c5f220>,\n",
       "  'dummy': <pycaret.containers.models.regression.DummyRegressorContainer at 0x1f420c31250>,\n",
       "  'Bagging': <pycaret.containers.models.regression.BaggingRegressorContainer at 0x1f420c3db50>,\n",
       "  'Stacking': <pycaret.containers.models.regression.StackingRegressorContainer at 0x1f420c1f3a0>,\n",
       "  'Voting': <pycaret.containers.models.regression.VotingRegressorContainer at 0x1f420c1f910>},\n",
       "            Open       High        Low       Volume       SMA7      SMA30\n",
       " 809    4.861416   4.861416   4.669681   44085700.0   4.880000   4.750000\n",
       " 403    7.697330   7.755335   7.482709   39540700.0   7.820000   8.270000\n",
       " 304   10.591805  10.644011  10.446792   22821900.0  10.700000  10.590000\n",
       " 1743  17.077955  17.168692  16.967775   38201600.0  17.410000  17.799999\n",
       " 866    2.925508   2.950248   2.721403  100576600.0   2.970000   3.900000\n",
       " ...         ...        ...        ...          ...        ...        ...\n",
       " 883    2.783253   2.826548   2.721403   56031000.0   2.780000   3.010000\n",
       " 319   11.577898  11.925932  11.514093   41785200.0  11.180000  10.840000\n",
       " 899    5.028411   5.071706   4.576906   86084704.0   4.690000   3.380000\n",
       " 2102  20.407734  20.707748  20.267727   48744700.0  20.040001  18.520000\n",
       " 1029   8.250798   8.300279   8.139468   44651900.0   8.350000   7.930000\n",
       " \n",
       " [660 rows x 6 columns],\n",
       " 'box-cox',\n",
       " {'lr': <pycaret.containers.models.regression.LinearRegressionContainer at 0x1f420ca8970>,\n",
       "  'lasso': <pycaret.containers.models.regression.LassoRegressionContainer at 0x1f420ca8c10>,\n",
       "  'ridge': <pycaret.containers.models.regression.RidgeRegressionContainer at 0x1f420c7e760>,\n",
       "  'en': <pycaret.containers.models.regression.ElasticNetContainer at 0x1f420c7ee20>,\n",
       "  'lar': <pycaret.containers.models.regression.LarsContainer at 0x1f420c2ddc0>,\n",
       "  'llar': <pycaret.containers.models.regression.LassoLarsContainer at 0x1f420c2d880>,\n",
       "  'omp': <pycaret.containers.models.regression.OrthogonalMatchingPursuitContainer at 0x1f420cca490>,\n",
       "  'br': <pycaret.containers.models.regression.BayesianRidgeContainer at 0x1f420cca310>,\n",
       "  'ard': <pycaret.containers.models.regression.AutomaticRelevanceDeterminationContainer at 0x1f420c9c430>,\n",
       "  'par': <pycaret.containers.models.regression.PassiveAggressiveRegressorContainer at 0x1f420ca25b0>,\n",
       "  'ransac': <pycaret.containers.models.regression.RANSACRegressorContainer at 0x1f420c95850>,\n",
       "  'tr': <pycaret.containers.models.regression.TheilSenRegressorContainer at 0x1f420c95be0>,\n",
       "  'huber': <pycaret.containers.models.regression.HuberRegressorContainer at 0x1f420c98bb0>,\n",
       "  'kr': <pycaret.containers.models.regression.KernelRidgeContainer at 0x1f420c989a0>,\n",
       "  'svm': <pycaret.containers.models.regression.SVRContainer at 0x1f420c98160>,\n",
       "  'knn': <pycaret.containers.models.regression.KNeighborsRegressorContainer at 0x1f420c8ee80>,\n",
       "  'dt': <pycaret.containers.models.regression.DecisionTreeRegressorContainer at 0x1f420c8edf0>,\n",
       "  'rf': <pycaret.containers.models.regression.RandomForestRegressorContainer at 0x1f420c37f10>,\n",
       "  'et': <pycaret.containers.models.regression.ExtraTreesRegressorContainer at 0x1f420c8afd0>,\n",
       "  'ada': <pycaret.containers.models.regression.AdaBoostRegressorContainer at 0x1f42080e640>,\n",
       "  'gbr': <pycaret.containers.models.regression.GradientBoostingRegressorContainer at 0x1f420776550>,\n",
       "  'mlp': <pycaret.containers.models.regression.MLPRegressorContainer at 0x1f420c55d60>,\n",
       "  'xgboost': <pycaret.containers.models.regression.XGBRegressorContainer at 0x1f420c55430>,\n",
       "  'lightgbm': <pycaret.containers.models.regression.LGBMRegressorContainer at 0x1f420c88be0>,\n",
       "  'catboost': <pycaret.containers.models.regression.CatBoostRegressorContainer at 0x1f420c5f7c0>,\n",
       "  'dummy': <pycaret.containers.models.regression.DummyRegressorContainer at 0x1f420c5f0a0>},\n",
       " None,\n",
       " Pipeline(memory=None,\n",
       "          steps=[('dtypes',\n",
       "                  DataTypes_Auto_infer(categorical_features=[],\n",
       "                                       display_types=True, features_todrop=[],\n",
       "                                       id_columns=[], ml_usecase='regression',\n",
       "                                       numerical_features=[], target='Close',\n",
       "                                       time_features=[])),\n",
       "                 ('imputer',\n",
       "                  Simple_Imputer(categorical_strategy='not_available',\n",
       "                                 fill_value_categorical=None,\n",
       "                                 fill_value_numerical=None,\n",
       "                                 numeric_strategy='...\n",
       "                 ('scaling', 'passthrough'), ('P_transform', 'passthrough'),\n",
       "                 ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),\n",
       "                 ('cluster_all', 'passthrough'),\n",
       "                 ('dummy', Dummify(target='Close')),\n",
       "                 ('fix_perfect', 'passthrough'),\n",
       "                 ('clean_names', Clean_Colum_Names()),\n",
       "                 ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
       "                 ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
       "          verbose=False),\n",
       " 'eb1e',\n",
       " False,\n",
       " KFold(n_splits=10, random_state=None, shuffle=False),\n",
       " 'lightgbm',\n",
       " -1,\n",
       " {'parameter': 'Hyperparameters',\n",
       "  'residuals': 'Residuals',\n",
       "  'error': 'Prediction Error',\n",
       "  'cooks': 'Cooks Distance',\n",
       "  'rfe': 'Feature Selection',\n",
       "  'learning': 'Learning Curve',\n",
       "  'manifold': 'Manifold Learning',\n",
       "  'vc': 'Validation Curve',\n",
       "  'feature': 'Feature Importance',\n",
       "  'feature_all': 'Feature Importance (All)',\n",
       "  'tree': 'Decision Tree',\n",
       "  'residuals_interactive': 'Interactive Residuals'},\n",
       " None,\n",
       " None,\n",
       " Pipeline(memory=None, steps=[('empty_step', 'passthrough')], verbose=False),\n",
       " 'lightgbm',\n",
       " False,\n",
       " {'USI',\n",
       "  'X',\n",
       "  'X_test',\n",
       "  'X_train',\n",
       "  '_all_metrics',\n",
       "  '_all_models',\n",
       "  '_all_models_internal',\n",
       "  '_available_plots',\n",
       "  '_gpu_n_jobs_param',\n",
       "  '_internal_pipeline',\n",
       "  '_ml_usecase',\n",
       "  'create_model_container',\n",
       "  'dashboard_logger',\n",
       "  'data_before_preprocess',\n",
       "  'display_container',\n",
       "  'exp_name_log',\n",
       "  'experiment__',\n",
       "  'fix_imbalance_method_param',\n",
       "  'fix_imbalance_param',\n",
       "  'fold_generator',\n",
       "  'fold_groups_param',\n",
       "  'fold_groups_param_full',\n",
       "  'fold_param',\n",
       "  'fold_shuffle_param',\n",
       "  'gpu_param',\n",
       "  'html_param',\n",
       "  'imputation_classifier',\n",
       "  'imputation_regressor',\n",
       "  'iterative_imputation_iters_param',\n",
       "  'log_plots_param',\n",
       "  'logging_param',\n",
       "  'master_model_container',\n",
       "  'n_jobs_param',\n",
       "  'prep_pipe',\n",
       "  'pycaret_globals',\n",
       "  'seed',\n",
       "  'stratify_param',\n",
       "  'target_param',\n",
       "  'transform_target_method_param',\n",
       "  'transform_target_param',\n",
       "  'y',\n",
       "  'y_test',\n",
       "  'y_train'},\n",
       " True,\n",
       " False,\n",
       " 210     11.514092\n",
       " 489     11.943244\n",
       " 297     10.823829\n",
       " 1371    11.961804\n",
       " 1642    17.227383\n",
       "           ...    \n",
       " 1147     8.850745\n",
       " 2154    15.887498\n",
       " 1766    16.656933\n",
       " 1122     9.289879\n",
       " 1346     9.364100\n",
       " Name: Close, Length: 1538, dtype: float32,\n",
       " [('Setup Config',\n",
       "                                  Description             Value\n",
       "   0                               session_id               123\n",
       "   1                                   Target             Close\n",
       "   2                            Original Data         (2198, 7)\n",
       "   3                           Missing Values             False\n",
       "   4                         Numeric Features                 6\n",
       "   5                     Categorical Features                 0\n",
       "   6                         Ordinal Features             False\n",
       "   7                High Cardinality Features             False\n",
       "   8                  High Cardinality Method              None\n",
       "   9                    Transformed Train Set         (1538, 6)\n",
       "   10                    Transformed Test Set          (660, 6)\n",
       "   11                      Shuffle Train-Test              True\n",
       "   12                     Stratify Train-Test             False\n",
       "   13                          Fold Generator             KFold\n",
       "   14                             Fold Number                10\n",
       "   15                                CPU Jobs                -1\n",
       "   16                                 Use GPU             False\n",
       "   17                          Log Experiment             False\n",
       "   18                         Experiment Name  reg-default-name\n",
       "   19                                     USI              eb1e\n",
       "   20                         Imputation Type            simple\n",
       "   21          Iterative Imputation Iteration              None\n",
       "   22                         Numeric Imputer              mean\n",
       "   23      Iterative Imputation Numeric Model              None\n",
       "   24                     Categorical Imputer          constant\n",
       "   25  Iterative Imputation Categorical Model              None\n",
       "   26           Unknown Categoricals Handling    least_frequent\n",
       "   27                               Normalize             False\n",
       "   28                        Normalize Method              None\n",
       "   29                          Transformation             False\n",
       "   30                   Transformation Method              None\n",
       "   31                                     PCA             False\n",
       "   32                              PCA Method              None\n",
       "   33                          PCA Components              None\n",
       "   34                     Ignore Low Variance             False\n",
       "   35                     Combine Rare Levels             False\n",
       "   36                    Rare Level Threshold              None\n",
       "   37                         Numeric Binning             False\n",
       "   38                         Remove Outliers             False\n",
       "   39                      Outliers Threshold              None\n",
       "   40                Remove Multicollinearity             False\n",
       "   41             Multicollinearity Threshold              None\n",
       "   42             Remove Perfect Collinearity             False\n",
       "   43                              Clustering             False\n",
       "   44                    Clustering Iteration              None\n",
       "   45                     Polynomial Features             False\n",
       "   46                       Polynomial Degree              None\n",
       "   47                    Trignometry Features             False\n",
       "   48                    Polynomial Threshold              None\n",
       "   49                          Group Features             False\n",
       "   50                       Feature Selection             False\n",
       "   51                Feature Selection Method           classic\n",
       "   52            Features Selection Threshold              None\n",
       "   53                     Feature Interaction             False\n",
       "   54                           Feature Ratio             False\n",
       "   55                   Interaction Threshold              None\n",
       "   56                        Transform Target             False\n",
       "   57                 Transform Target Method           box-cox),\n",
       "  ('X_training Set',\n",
       "              Open       High        Low      Volume       SMA7  SMA30\n",
       "   210   11.554696  11.641705  11.485089  24961000.0  11.600000  11.34\n",
       "   489   11.967984  11.992724  11.800989  27591400.0  11.370000  11.06\n",
       "   297   10.748422  10.841230  10.655613  15455100.0  10.840000  10.48\n",
       "   1371  11.392784  12.141169  11.392784  89768200.0  11.360000  10.32\n",
       "   1642  17.511278  17.582253  17.182217  41978700.0  17.389999  16.82\n",
       "   ...         ...        ...        ...         ...        ...    ...\n",
       "   1147   9.104330   9.203290   8.807449  42454600.0   9.200000   9.46\n",
       "   2154  15.560814  15.887498  15.520813  50099700.0  15.580000  15.76\n",
       "   1766  16.676529  16.781044  16.441372  54958200.0  16.180000  16.65\n",
       "   1122   9.438319   9.531094   9.221844  35977000.0   9.570000   9.44\n",
       "   1346   9.388840   9.432135   9.314619  22639700.0   9.400000   9.71\n",
       "   \n",
       "   [1538 rows x 6 columns]),\n",
       "  ('y_training Set',\n",
       "   210     11.514092\n",
       "   489     11.943244\n",
       "   297     10.823829\n",
       "   1371    11.961804\n",
       "   1642    17.227383\n",
       "             ...    \n",
       "   1147     8.850745\n",
       "   2154    15.887498\n",
       "   1766    16.656933\n",
       "   1122     9.289879\n",
       "   1346     9.364100\n",
       "   Name: Close, Length: 1538, dtype: float32),\n",
       "  ('X_test Set',\n",
       "              Open       High        Low       Volume       SMA7      SMA30\n",
       "   809    4.861416   4.861416   4.669681   44085700.0   4.880000   4.750000\n",
       "   403    7.697330   7.755335   7.482709   39540700.0   7.820000   8.270000\n",
       "   304   10.591805  10.644011  10.446792   22821900.0  10.700000  10.590000\n",
       "   1743  17.077955  17.168692  16.967775   38201600.0  17.410000  17.799999\n",
       "   866    2.925508   2.950248   2.721403  100576600.0   2.970000   3.900000\n",
       "   ...         ...        ...        ...          ...        ...        ...\n",
       "   883    2.783253   2.826548   2.721403   56031000.0   2.780000   3.010000\n",
       "   319   11.577898  11.925932  11.514093   41785200.0  11.180000  10.840000\n",
       "   899    5.028411   5.071706   4.576906   86084704.0   4.690000   3.380000\n",
       "   2102  20.407734  20.707748  20.267727   48744700.0  20.040001  18.520000\n",
       "   1029   8.250798   8.300279   8.139468   44651900.0   8.350000   7.930000\n",
       "   \n",
       "   [660 rows x 6 columns]),\n",
       "  ('y_test Set',\n",
       "   809      4.700606\n",
       "   403      7.546515\n",
       "   304     10.527999\n",
       "   1743    17.006662\n",
       "   866      2.727588\n",
       "             ...    \n",
       "   883      2.758513\n",
       "   319     11.769318\n",
       "   899      4.576906\n",
       "   2102    20.574409\n",
       "   1029     8.139468\n",
       "   Name: Close, Length: 660, dtype: float32),\n",
       "  ('Transformation Pipeline',\n",
       "   Pipeline(memory=None,\n",
       "            steps=[('dtypes',\n",
       "                    DataTypes_Auto_infer(categorical_features=[],\n",
       "                                         display_types=True, features_todrop=[],\n",
       "                                         id_columns=[], ml_usecase='regression',\n",
       "                                         numerical_features=[], target='Close',\n",
       "                                         time_features=[])),\n",
       "                   ('imputer',\n",
       "                    Simple_Imputer(categorical_strategy='not_available',\n",
       "                                   fill_value_categorical=None,\n",
       "                                   fill_value_numerical=None,\n",
       "                                   numeric_strategy='...\n",
       "                   ('scaling', 'passthrough'), ('P_transform', 'passthrough'),\n",
       "                   ('binn', 'passthrough'), ('rem_outliers', 'passthrough'),\n",
       "                   ('cluster_all', 'passthrough'),\n",
       "                   ('dummy', Dummify(target='Close')),\n",
       "                   ('fix_perfect', 'passthrough'),\n",
       "                   ('clean_names', Clean_Colum_Names()),\n",
       "                   ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
       "                   ('dfs', 'passthrough'), ('pca', 'passthrough')],\n",
       "            verbose=False))],\n",
       "            Open       High        Low      Close     Volume   SMA7  SMA30\n",
       " 0     11.000421  11.250819  11.000421  11.165457   20781700  10.93  10.71\n",
       " 1     11.028874  11.131309  10.875221  10.915057   19025100  10.98  10.72\n",
       " 2     10.738643  10.835388  10.476863  10.789860   21365500  11.01  10.72\n",
       " 3     10.841077  10.926441  10.602062  10.693116   19547500  10.95  10.72\n",
       " 4     10.812622  10.932129  10.670351  10.812622   17749100  10.90  10.73\n",
       " ...         ...        ...        ...        ...        ...    ...    ...\n",
       " 2193  18.248424  18.344905  17.766027  17.807375   63672000  18.04  16.85\n",
       " 2194  17.855615  18.041683  17.676438  17.979660   46646900  17.99  16.92\n",
       " 2195  17.972768  18.089921  17.759135  17.855614   48877800  17.94  16.97\n",
       " 2196  18.055466  18.620561  18.021008  18.599886  129526400  18.03  17.06\n",
       " 2197  18.579212  18.744607  18.455168  18.517191   46298900  18.12  17.15\n",
       " \n",
       " [2198 rows x 7 columns],\n",
       " False,\n",
       " <MLUsecase.REGRESSION: 2>,\n",
       " [],\n",
       " False,\n",
       " 10,\n",
       " 'Close')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importando biblioteca de regressão do Pycaret\n",
    "from pycaret.regression import *\n",
    "# Configurando o Pycaret para receber nossos dados para modelagem\n",
    "# Session_id para reproduzir as mesmas informações\n",
    "# remove_perfect_collinearity => Não remover colunas com alta correlação\n",
    "setup(data = petr4, target = 'Close', session_id=123, remove_perfect_collinearity = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "863dbbec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "}\n",
       "</style>\n",
       "<table id=\"T_2ede1\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2ede1_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_2ede1_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_2ede1_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_2ede1_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_2ede1_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_2ede1_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_2ede1_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "      <th id=\"T_2ede1_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row0\" class=\"row_heading level0 row0\" >ridge</th>\n",
       "      <td id=\"T_2ede1_row0_col0\" class=\"data row0 col0\" >Ridge Regression</td>\n",
       "      <td id=\"T_2ede1_row0_col1\" class=\"data row0 col1\" >0.0854</td>\n",
       "      <td id=\"T_2ede1_row0_col2\" class=\"data row0 col2\" >0.0141</td>\n",
       "      <td id=\"T_2ede1_row0_col3\" class=\"data row0 col3\" >0.1180</td>\n",
       "      <td id=\"T_2ede1_row0_col4\" class=\"data row0 col4\" >0.9992</td>\n",
       "      <td id=\"T_2ede1_row0_col5\" class=\"data row0 col5\" >0.0100</td>\n",
       "      <td id=\"T_2ede1_row0_col6\" class=\"data row0 col6\" >0.0081</td>\n",
       "      <td id=\"T_2ede1_row0_col7\" class=\"data row0 col7\" >0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row1\" class=\"row_heading level0 row1\" >lar</th>\n",
       "      <td id=\"T_2ede1_row1_col0\" class=\"data row1 col0\" >Least Angle Regression</td>\n",
       "      <td id=\"T_2ede1_row1_col1\" class=\"data row1 col1\" >0.0845</td>\n",
       "      <td id=\"T_2ede1_row1_col2\" class=\"data row1 col2\" >0.0142</td>\n",
       "      <td id=\"T_2ede1_row1_col3\" class=\"data row1 col3\" >0.1181</td>\n",
       "      <td id=\"T_2ede1_row1_col4\" class=\"data row1 col4\" >0.9992</td>\n",
       "      <td id=\"T_2ede1_row1_col5\" class=\"data row1 col5\" >0.0100</td>\n",
       "      <td id=\"T_2ede1_row1_col6\" class=\"data row1 col6\" >0.0080</td>\n",
       "      <td id=\"T_2ede1_row1_col7\" class=\"data row1 col7\" >0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row2\" class=\"row_heading level0 row2\" >br</th>\n",
       "      <td id=\"T_2ede1_row2_col0\" class=\"data row2 col0\" >Bayesian Ridge</td>\n",
       "      <td id=\"T_2ede1_row2_col1\" class=\"data row2 col1\" >0.0846</td>\n",
       "      <td id=\"T_2ede1_row2_col2\" class=\"data row2 col2\" >0.0141</td>\n",
       "      <td id=\"T_2ede1_row2_col3\" class=\"data row2 col3\" >0.1178</td>\n",
       "      <td id=\"T_2ede1_row2_col4\" class=\"data row2 col4\" >0.9992</td>\n",
       "      <td id=\"T_2ede1_row2_col5\" class=\"data row2 col5\" >0.0100</td>\n",
       "      <td id=\"T_2ede1_row2_col6\" class=\"data row2 col6\" >0.0080</td>\n",
       "      <td id=\"T_2ede1_row2_col7\" class=\"data row2 col7\" >0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row3\" class=\"row_heading level0 row3\" >et</th>\n",
       "      <td id=\"T_2ede1_row3_col0\" class=\"data row3 col0\" >Extra Trees Regressor</td>\n",
       "      <td id=\"T_2ede1_row3_col1\" class=\"data row3 col1\" >0.1102</td>\n",
       "      <td id=\"T_2ede1_row3_col2\" class=\"data row3 col2\" >0.0233</td>\n",
       "      <td id=\"T_2ede1_row3_col3\" class=\"data row3 col3\" >0.1523</td>\n",
       "      <td id=\"T_2ede1_row3_col4\" class=\"data row3 col4\" >0.9987</td>\n",
       "      <td id=\"T_2ede1_row3_col5\" class=\"data row3 col5\" >0.0130</td>\n",
       "      <td id=\"T_2ede1_row3_col6\" class=\"data row3 col6\" >0.0105</td>\n",
       "      <td id=\"T_2ede1_row3_col7\" class=\"data row3 col7\" >0.1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row4\" class=\"row_heading level0 row4\" >rf</th>\n",
       "      <td id=\"T_2ede1_row4_col0\" class=\"data row4 col0\" >Random Forest Regressor</td>\n",
       "      <td id=\"T_2ede1_row4_col1\" class=\"data row4 col1\" >0.1143</td>\n",
       "      <td id=\"T_2ede1_row4_col2\" class=\"data row4 col2\" >0.0247</td>\n",
       "      <td id=\"T_2ede1_row4_col3\" class=\"data row4 col3\" >0.1566</td>\n",
       "      <td id=\"T_2ede1_row4_col4\" class=\"data row4 col4\" >0.9986</td>\n",
       "      <td id=\"T_2ede1_row4_col5\" class=\"data row4 col5\" >0.0133</td>\n",
       "      <td id=\"T_2ede1_row4_col6\" class=\"data row4 col6\" >0.0109</td>\n",
       "      <td id=\"T_2ede1_row4_col7\" class=\"data row4 col7\" >0.1440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row5\" class=\"row_heading level0 row5\" >lightgbm</th>\n",
       "      <td id=\"T_2ede1_row5_col0\" class=\"data row5 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_2ede1_row5_col1\" class=\"data row5 col1\" >0.1231</td>\n",
       "      <td id=\"T_2ede1_row5_col2\" class=\"data row5 col2\" >0.0284</td>\n",
       "      <td id=\"T_2ede1_row5_col3\" class=\"data row5 col3\" >0.1683</td>\n",
       "      <td id=\"T_2ede1_row5_col4\" class=\"data row5 col4\" >0.9984</td>\n",
       "      <td id=\"T_2ede1_row5_col5\" class=\"data row5 col5\" >0.0171</td>\n",
       "      <td id=\"T_2ede1_row5_col6\" class=\"data row5 col6\" >0.0126</td>\n",
       "      <td id=\"T_2ede1_row5_col7\" class=\"data row5 col7\" >0.0490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row6\" class=\"row_heading level0 row6\" >gbr</th>\n",
       "      <td id=\"T_2ede1_row6_col0\" class=\"data row6 col0\" >Gradient Boosting Regressor</td>\n",
       "      <td id=\"T_2ede1_row6_col1\" class=\"data row6 col1\" >0.1284</td>\n",
       "      <td id=\"T_2ede1_row6_col2\" class=\"data row6 col2\" >0.0295</td>\n",
       "      <td id=\"T_2ede1_row6_col3\" class=\"data row6 col3\" >0.1712</td>\n",
       "      <td id=\"T_2ede1_row6_col4\" class=\"data row6 col4\" >0.9983</td>\n",
       "      <td id=\"T_2ede1_row6_col5\" class=\"data row6 col5\" >0.0147</td>\n",
       "      <td id=\"T_2ede1_row6_col6\" class=\"data row6 col6\" >0.0123</td>\n",
       "      <td id=\"T_2ede1_row6_col7\" class=\"data row6 col7\" >0.0950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row7\" class=\"row_heading level0 row7\" >omp</th>\n",
       "      <td id=\"T_2ede1_row7_col0\" class=\"data row7 col0\" >Orthogonal Matching Pursuit</td>\n",
       "      <td id=\"T_2ede1_row7_col1\" class=\"data row7 col1\" >0.1278</td>\n",
       "      <td id=\"T_2ede1_row7_col2\" class=\"data row7 col2\" >0.0314</td>\n",
       "      <td id=\"T_2ede1_row7_col3\" class=\"data row7 col3\" >0.1758</td>\n",
       "      <td id=\"T_2ede1_row7_col4\" class=\"data row7 col4\" >0.9982</td>\n",
       "      <td id=\"T_2ede1_row7_col5\" class=\"data row7 col5\" >0.0153</td>\n",
       "      <td id=\"T_2ede1_row7_col6\" class=\"data row7 col6\" >0.0123</td>\n",
       "      <td id=\"T_2ede1_row7_col7\" class=\"data row7 col7\" >0.0040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row8\" class=\"row_heading level0 row8\" >xgboost</th>\n",
       "      <td id=\"T_2ede1_row8_col0\" class=\"data row8 col0\" >Extreme Gradient Boosting</td>\n",
       "      <td id=\"T_2ede1_row8_col1\" class=\"data row8 col1\" >0.1303</td>\n",
       "      <td id=\"T_2ede1_row8_col2\" class=\"data row8 col2\" >0.0328</td>\n",
       "      <td id=\"T_2ede1_row8_col3\" class=\"data row8 col3\" >0.1799</td>\n",
       "      <td id=\"T_2ede1_row8_col4\" class=\"data row8 col4\" >0.9981</td>\n",
       "      <td id=\"T_2ede1_row8_col5\" class=\"data row8 col5\" >0.0153</td>\n",
       "      <td id=\"T_2ede1_row8_col6\" class=\"data row8 col6\" >0.0124</td>\n",
       "      <td id=\"T_2ede1_row8_col7\" class=\"data row8 col7\" >0.1760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row9\" class=\"row_heading level0 row9\" >catboost</th>\n",
       "      <td id=\"T_2ede1_row9_col0\" class=\"data row9 col0\" >CatBoost Regressor</td>\n",
       "      <td id=\"T_2ede1_row9_col1\" class=\"data row9 col1\" >0.1383</td>\n",
       "      <td id=\"T_2ede1_row9_col2\" class=\"data row9 col2\" >0.0362</td>\n",
       "      <td id=\"T_2ede1_row9_col3\" class=\"data row9 col3\" >0.1898</td>\n",
       "      <td id=\"T_2ede1_row9_col4\" class=\"data row9 col4\" >0.9979</td>\n",
       "      <td id=\"T_2ede1_row9_col5\" class=\"data row9 col5\" >0.0165</td>\n",
       "      <td id=\"T_2ede1_row9_col6\" class=\"data row9 col6\" >0.0134</td>\n",
       "      <td id=\"T_2ede1_row9_col7\" class=\"data row9 col7\" >1.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row10\" class=\"row_heading level0 row10\" >dt</th>\n",
       "      <td id=\"T_2ede1_row10_col0\" class=\"data row10 col0\" >Decision Tree Regressor</td>\n",
       "      <td id=\"T_2ede1_row10_col1\" class=\"data row10 col1\" >0.1504</td>\n",
       "      <td id=\"T_2ede1_row10_col2\" class=\"data row10 col2\" >0.0441</td>\n",
       "      <td id=\"T_2ede1_row10_col3\" class=\"data row10 col3\" >0.2088</td>\n",
       "      <td id=\"T_2ede1_row10_col4\" class=\"data row10 col4\" >0.9975</td>\n",
       "      <td id=\"T_2ede1_row10_col5\" class=\"data row10 col5\" >0.0186</td>\n",
       "      <td id=\"T_2ede1_row10_col6\" class=\"data row10 col6\" >0.0146</td>\n",
       "      <td id=\"T_2ede1_row10_col7\" class=\"data row10 col7\" >0.0060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row11\" class=\"row_heading level0 row11\" >en</th>\n",
       "      <td id=\"T_2ede1_row11_col0\" class=\"data row11 col0\" >Elastic Net</td>\n",
       "      <td id=\"T_2ede1_row11_col1\" class=\"data row11 col1\" >0.1954</td>\n",
       "      <td id=\"T_2ede1_row11_col2\" class=\"data row11 col2\" >0.0644</td>\n",
       "      <td id=\"T_2ede1_row11_col3\" class=\"data row11 col3\" >0.2533</td>\n",
       "      <td id=\"T_2ede1_row11_col4\" class=\"data row11 col4\" >0.9963</td>\n",
       "      <td id=\"T_2ede1_row11_col5\" class=\"data row11 col5\" >0.0245</td>\n",
       "      <td id=\"T_2ede1_row11_col6\" class=\"data row11 col6\" >0.0203</td>\n",
       "      <td id=\"T_2ede1_row11_col7\" class=\"data row11 col7\" >0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row12\" class=\"row_heading level0 row12\" >ada</th>\n",
       "      <td id=\"T_2ede1_row12_col0\" class=\"data row12 col0\" >AdaBoost Regressor</td>\n",
       "      <td id=\"T_2ede1_row12_col1\" class=\"data row12 col1\" >0.2000</td>\n",
       "      <td id=\"T_2ede1_row12_col2\" class=\"data row12 col2\" >0.0666</td>\n",
       "      <td id=\"T_2ede1_row12_col3\" class=\"data row12 col3\" >0.2575</td>\n",
       "      <td id=\"T_2ede1_row12_col4\" class=\"data row12 col4\" >0.9962</td>\n",
       "      <td id=\"T_2ede1_row12_col5\" class=\"data row12 col5\" >0.0285</td>\n",
       "      <td id=\"T_2ede1_row12_col6\" class=\"data row12 col6\" >0.0221</td>\n",
       "      <td id=\"T_2ede1_row12_col7\" class=\"data row12 col7\" >0.0840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row13\" class=\"row_heading level0 row13\" >lasso</th>\n",
       "      <td id=\"T_2ede1_row13_col0\" class=\"data row13 col0\" >Lasso Regression</td>\n",
       "      <td id=\"T_2ede1_row13_col1\" class=\"data row13 col1\" >0.2333</td>\n",
       "      <td id=\"T_2ede1_row13_col2\" class=\"data row13 col2\" >0.0886</td>\n",
       "      <td id=\"T_2ede1_row13_col3\" class=\"data row13 col3\" >0.2968</td>\n",
       "      <td id=\"T_2ede1_row13_col4\" class=\"data row13 col4\" >0.9950</td>\n",
       "      <td id=\"T_2ede1_row13_col5\" class=\"data row13 col5\" >0.0306</td>\n",
       "      <td id=\"T_2ede1_row13_col6\" class=\"data row13 col6\" >0.0252</td>\n",
       "      <td id=\"T_2ede1_row13_col7\" class=\"data row13 col7\" >0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row14\" class=\"row_heading level0 row14\" >par</th>\n",
       "      <td id=\"T_2ede1_row14_col0\" class=\"data row14 col0\" >Passive Aggressive Regressor</td>\n",
       "      <td id=\"T_2ede1_row14_col1\" class=\"data row14 col1\" >0.3037</td>\n",
       "      <td id=\"T_2ede1_row14_col2\" class=\"data row14 col2\" >0.1797</td>\n",
       "      <td id=\"T_2ede1_row14_col3\" class=\"data row14 col3\" >0.4191</td>\n",
       "      <td id=\"T_2ede1_row14_col4\" class=\"data row14 col4\" >0.9895</td>\n",
       "      <td id=\"T_2ede1_row14_col5\" class=\"data row14 col5\" >0.0386</td>\n",
       "      <td id=\"T_2ede1_row14_col6\" class=\"data row14 col6\" >0.0302</td>\n",
       "      <td id=\"T_2ede1_row14_col7\" class=\"data row14 col7\" >0.0060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row15\" class=\"row_heading level0 row15\" >lr</th>\n",
       "      <td id=\"T_2ede1_row15_col0\" class=\"data row15 col0\" >Linear Regression</td>\n",
       "      <td id=\"T_2ede1_row15_col1\" class=\"data row15 col1\" >0.3902</td>\n",
       "      <td id=\"T_2ede1_row15_col2\" class=\"data row15 col2\" >0.4325</td>\n",
       "      <td id=\"T_2ede1_row15_col3\" class=\"data row15 col3\" >0.6190</td>\n",
       "      <td id=\"T_2ede1_row15_col4\" class=\"data row15 col4\" >0.9753</td>\n",
       "      <td id=\"T_2ede1_row15_col5\" class=\"data row15 col5\" >0.0540</td>\n",
       "      <td id=\"T_2ede1_row15_col6\" class=\"data row15 col6\" >0.0402</td>\n",
       "      <td id=\"T_2ede1_row15_col7\" class=\"data row15 col7\" >0.7360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row16\" class=\"row_heading level0 row16\" >llar</th>\n",
       "      <td id=\"T_2ede1_row16_col0\" class=\"data row16 col0\" >Lasso Least Angle Regression</td>\n",
       "      <td id=\"T_2ede1_row16_col1\" class=\"data row16 col1\" >3.4079</td>\n",
       "      <td id=\"T_2ede1_row16_col2\" class=\"data row16 col2\" >17.6500</td>\n",
       "      <td id=\"T_2ede1_row16_col3\" class=\"data row16 col3\" >4.1981</td>\n",
       "      <td id=\"T_2ede1_row16_col4\" class=\"data row16 col4\" >-0.0092</td>\n",
       "      <td id=\"T_2ede1_row16_col5\" class=\"data row16 col5\" >0.3729</td>\n",
       "      <td id=\"T_2ede1_row16_col6\" class=\"data row16 col6\" >0.3878</td>\n",
       "      <td id=\"T_2ede1_row16_col7\" class=\"data row16 col7\" >0.0040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row17\" class=\"row_heading level0 row17\" >dummy</th>\n",
       "      <td id=\"T_2ede1_row17_col0\" class=\"data row17 col0\" >Dummy Regressor</td>\n",
       "      <td id=\"T_2ede1_row17_col1\" class=\"data row17 col1\" >3.4079</td>\n",
       "      <td id=\"T_2ede1_row17_col2\" class=\"data row17 col2\" >17.6500</td>\n",
       "      <td id=\"T_2ede1_row17_col3\" class=\"data row17 col3\" >4.1981</td>\n",
       "      <td id=\"T_2ede1_row17_col4\" class=\"data row17 col4\" >-0.0092</td>\n",
       "      <td id=\"T_2ede1_row17_col5\" class=\"data row17 col5\" >0.3729</td>\n",
       "      <td id=\"T_2ede1_row17_col6\" class=\"data row17 col6\" >0.3878</td>\n",
       "      <td id=\"T_2ede1_row17_col7\" class=\"data row17 col7\" >0.0070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row18\" class=\"row_heading level0 row18\" >knn</th>\n",
       "      <td id=\"T_2ede1_row18_col0\" class=\"data row18 col0\" >K Neighbors Regressor</td>\n",
       "      <td id=\"T_2ede1_row18_col1\" class=\"data row18 col1\" >3.6511</td>\n",
       "      <td id=\"T_2ede1_row18_col2\" class=\"data row18 col2\" >20.9018</td>\n",
       "      <td id=\"T_2ede1_row18_col3\" class=\"data row18 col3\" >4.5695</td>\n",
       "      <td id=\"T_2ede1_row18_col4\" class=\"data row18 col4\" >-0.2003</td>\n",
       "      <td id=\"T_2ede1_row18_col5\" class=\"data row18 col5\" >0.3994</td>\n",
       "      <td id=\"T_2ede1_row18_col6\" class=\"data row18 col6\" >0.4115</td>\n",
       "      <td id=\"T_2ede1_row18_col7\" class=\"data row18 col7\" >0.0070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2ede1_level0_row19\" class=\"row_heading level0 row19\" >huber</th>\n",
       "      <td id=\"T_2ede1_row19_col0\" class=\"data row19 col0\" >Huber Regressor</td>\n",
       "      <td id=\"T_2ede1_row19_col1\" class=\"data row19 col1\" >5.7496</td>\n",
       "      <td id=\"T_2ede1_row19_col2\" class=\"data row19 col2\" >66.7080</td>\n",
       "      <td id=\"T_2ede1_row19_col3\" class=\"data row19 col3\" >7.9758</td>\n",
       "      <td id=\"T_2ede1_row19_col4\" class=\"data row19 col4\" >-2.8032</td>\n",
       "      <td id=\"T_2ede1_row19_col5\" class=\"data row19 col5\" >0.6747</td>\n",
       "      <td id=\"T_2ede1_row19_col6\" class=\"data row19 col6\" >0.5955</td>\n",
       "      <td id=\"T_2ede1_row19_col7\" class=\"data row19 col7\" >0.0050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f420c52dc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparar os modelos e informar os três melhores\n",
    "top3 = compare_models(n_select=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c612be4f",
   "metadata": {},
   "source": [
    "MAE, MSE, RMSE, R2, RMSLE e MAPE são todas métricas utilizadas para avaliar a acurácia do nosso modelo.<br>\n",
    "A tabela acima organiza o *ranking* de modelos se baseando no coeficiente de determinação R², que é uma métrica muito utilizada para observar o quão bem o modelo se ajusta ao conjunto de dados utilizado. Os três melhores modelos possuem o mesmo R² de 0,9992, o que significa que 99.92% do dados se ajustam bem aos modelos testados pelo PyCaret.<br>\n",
    "Dentre os três melhores modelos, é possível observar que o **Bayesian Ridge** foi o que teve as melhores performances em todas as métricas, perdendo apenas para o modelo **Least Angle Regression** na métrica MAE."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c6ffb5f",
   "metadata": {},
   "source": [
    "## Testando os três melhores modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "91a4a0b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_32028_row10_col0, #T_32028_row10_col1, #T_32028_row10_col2, #T_32028_row10_col3, #T_32028_row10_col4, #T_32028_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_32028\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_32028_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_32028_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_32028_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_32028_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_32028_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_32028_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_32028_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_32028_row0_col0\" class=\"data row0 col0\" >0.0883</td>\n",
       "      <td id=\"T_32028_row0_col1\" class=\"data row0 col1\" >0.0157</td>\n",
       "      <td id=\"T_32028_row0_col2\" class=\"data row0 col2\" >0.1252</td>\n",
       "      <td id=\"T_32028_row0_col3\" class=\"data row0 col3\" >0.9992</td>\n",
       "      <td id=\"T_32028_row0_col4\" class=\"data row0 col4\" >0.0106</td>\n",
       "      <td id=\"T_32028_row0_col5\" class=\"data row0 col5\" >0.0087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32028_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_32028_row1_col0\" class=\"data row1 col0\" >0.0880</td>\n",
       "      <td id=\"T_32028_row1_col1\" class=\"data row1 col1\" >0.0146</td>\n",
       "      <td id=\"T_32028_row1_col2\" class=\"data row1 col2\" >0.1209</td>\n",
       "      <td id=\"T_32028_row1_col3\" class=\"data row1 col3\" >0.9992</td>\n",
       "      <td id=\"T_32028_row1_col4\" class=\"data row1 col4\" >0.0096</td>\n",
       "      <td id=\"T_32028_row1_col5\" class=\"data row1 col5\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32028_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_32028_row2_col0\" class=\"data row2 col0\" >0.0804</td>\n",
       "      <td id=\"T_32028_row2_col1\" class=\"data row2 col1\" >0.0104</td>\n",
       "      <td id=\"T_32028_row2_col2\" class=\"data row2 col2\" >0.1020</td>\n",
       "      <td id=\"T_32028_row2_col3\" class=\"data row2 col3\" >0.9994</td>\n",
       "      <td id=\"T_32028_row2_col4\" class=\"data row2 col4\" >0.0094</td>\n",
       "      <td id=\"T_32028_row2_col5\" class=\"data row2 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32028_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_32028_row3_col0\" class=\"data row3 col0\" >0.0895</td>\n",
       "      <td id=\"T_32028_row3_col1\" class=\"data row3 col1\" >0.0248</td>\n",
       "      <td id=\"T_32028_row3_col2\" class=\"data row3 col2\" >0.1576</td>\n",
       "      <td id=\"T_32028_row3_col3\" class=\"data row3 col3\" >0.9984</td>\n",
       "      <td id=\"T_32028_row3_col4\" class=\"data row3 col4\" >0.0140</td>\n",
       "      <td id=\"T_32028_row3_col5\" class=\"data row3 col5\" >0.0084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32028_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_32028_row4_col0\" class=\"data row4 col0\" >0.0744</td>\n",
       "      <td id=\"T_32028_row4_col1\" class=\"data row4 col1\" >0.0102</td>\n",
       "      <td id=\"T_32028_row4_col2\" class=\"data row4 col2\" >0.1010</td>\n",
       "      <td id=\"T_32028_row4_col3\" class=\"data row4 col3\" >0.9994</td>\n",
       "      <td id=\"T_32028_row4_col4\" class=\"data row4 col4\" >0.0079</td>\n",
       "      <td id=\"T_32028_row4_col5\" class=\"data row4 col5\" >0.0067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32028_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_32028_row5_col0\" class=\"data row5 col0\" >0.0891</td>\n",
       "      <td id=\"T_32028_row5_col1\" class=\"data row5 col1\" >0.0142</td>\n",
       "      <td id=\"T_32028_row5_col2\" class=\"data row5 col2\" >0.1193</td>\n",
       "      <td id=\"T_32028_row5_col3\" class=\"data row5 col3\" >0.9992</td>\n",
       "      <td id=\"T_32028_row5_col4\" class=\"data row5 col4\" >0.0094</td>\n",
       "      <td id=\"T_32028_row5_col5\" class=\"data row5 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32028_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_32028_row6_col0\" class=\"data row6 col0\" >0.0854</td>\n",
       "      <td id=\"T_32028_row6_col1\" class=\"data row6 col1\" >0.0120</td>\n",
       "      <td id=\"T_32028_row6_col2\" class=\"data row6 col2\" >0.1095</td>\n",
       "      <td id=\"T_32028_row6_col3\" class=\"data row6 col3\" >0.9994</td>\n",
       "      <td id=\"T_32028_row6_col4\" class=\"data row6 col4\" >0.0090</td>\n",
       "      <td id=\"T_32028_row6_col5\" class=\"data row6 col5\" >0.0078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32028_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_32028_row7_col0\" class=\"data row7 col0\" >0.0836</td>\n",
       "      <td id=\"T_32028_row7_col1\" class=\"data row7 col1\" >0.0121</td>\n",
       "      <td id=\"T_32028_row7_col2\" class=\"data row7 col2\" >0.1099</td>\n",
       "      <td id=\"T_32028_row7_col3\" class=\"data row7 col3\" >0.9994</td>\n",
       "      <td id=\"T_32028_row7_col4\" class=\"data row7 col4\" >0.0104</td>\n",
       "      <td id=\"T_32028_row7_col5\" class=\"data row7 col5\" >0.0087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32028_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_32028_row8_col0\" class=\"data row8 col0\" >0.0814</td>\n",
       "      <td id=\"T_32028_row8_col1\" class=\"data row8 col1\" >0.0120</td>\n",
       "      <td id=\"T_32028_row8_col2\" class=\"data row8 col2\" >0.1094</td>\n",
       "      <td id=\"T_32028_row8_col3\" class=\"data row8 col3\" >0.9993</td>\n",
       "      <td id=\"T_32028_row8_col4\" class=\"data row8 col4\" >0.0094</td>\n",
       "      <td id=\"T_32028_row8_col5\" class=\"data row8 col5\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32028_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_32028_row9_col0\" class=\"data row9 col0\" >0.0859</td>\n",
       "      <td id=\"T_32028_row9_col1\" class=\"data row9 col1\" >0.0152</td>\n",
       "      <td id=\"T_32028_row9_col2\" class=\"data row9 col2\" >0.1232</td>\n",
       "      <td id=\"T_32028_row9_col3\" class=\"data row9 col3\" >0.9990</td>\n",
       "      <td id=\"T_32028_row9_col4\" class=\"data row9 col4\" >0.0104</td>\n",
       "      <td id=\"T_32028_row9_col5\" class=\"data row9 col5\" >0.0079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32028_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_32028_row10_col0\" class=\"data row10 col0\" >0.0846</td>\n",
       "      <td id=\"T_32028_row10_col1\" class=\"data row10 col1\" >0.0141</td>\n",
       "      <td id=\"T_32028_row10_col2\" class=\"data row10 col2\" >0.1178</td>\n",
       "      <td id=\"T_32028_row10_col3\" class=\"data row10 col3\" >0.9992</td>\n",
       "      <td id=\"T_32028_row10_col4\" class=\"data row10 col4\" >0.0100</td>\n",
       "      <td id=\"T_32028_row10_col5\" class=\"data row10 col5\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32028_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_32028_row11_col0\" class=\"data row11 col0\" >0.0045</td>\n",
       "      <td id=\"T_32028_row11_col1\" class=\"data row11 col1\" >0.0040</td>\n",
       "      <td id=\"T_32028_row11_col2\" class=\"data row11 col2\" >0.0155</td>\n",
       "      <td id=\"T_32028_row11_col3\" class=\"data row11 col3\" >0.0003</td>\n",
       "      <td id=\"T_32028_row11_col4\" class=\"data row11 col4\" >0.0015</td>\n",
       "      <td id=\"T_32028_row11_col5\" class=\"data row11 col5\" >0.0005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f420cac070>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testando os modelos 10 vezes e vendo as médias das métricas de acurácia\n",
    "br = create_model('br', fold=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8a5f8b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7d699_row10_col0, #T_7d699_row10_col1, #T_7d699_row10_col2, #T_7d699_row10_col3, #T_7d699_row10_col4, #T_7d699_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7d699\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7d699_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_7d699_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_7d699_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_7d699_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_7d699_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_7d699_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7d699_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7d699_row0_col0\" class=\"data row0 col0\" >0.0888</td>\n",
       "      <td id=\"T_7d699_row0_col1\" class=\"data row0 col1\" >0.0156</td>\n",
       "      <td id=\"T_7d699_row0_col2\" class=\"data row0 col2\" >0.1249</td>\n",
       "      <td id=\"T_7d699_row0_col3\" class=\"data row0 col3\" >0.9992</td>\n",
       "      <td id=\"T_7d699_row0_col4\" class=\"data row0 col4\" >0.0105</td>\n",
       "      <td id=\"T_7d699_row0_col5\" class=\"data row0 col5\" >0.0088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d699_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_7d699_row1_col0\" class=\"data row1 col0\" >0.0884</td>\n",
       "      <td id=\"T_7d699_row1_col1\" class=\"data row1 col1\" >0.0145</td>\n",
       "      <td id=\"T_7d699_row1_col2\" class=\"data row1 col2\" >0.1205</td>\n",
       "      <td id=\"T_7d699_row1_col3\" class=\"data row1 col3\" >0.9992</td>\n",
       "      <td id=\"T_7d699_row1_col4\" class=\"data row1 col4\" >0.0096</td>\n",
       "      <td id=\"T_7d699_row1_col5\" class=\"data row1 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d699_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_7d699_row2_col0\" class=\"data row2 col0\" >0.0809</td>\n",
       "      <td id=\"T_7d699_row2_col1\" class=\"data row2 col1\" >0.0104</td>\n",
       "      <td id=\"T_7d699_row2_col2\" class=\"data row2 col2\" >0.1021</td>\n",
       "      <td id=\"T_7d699_row2_col3\" class=\"data row2 col3\" >0.9994</td>\n",
       "      <td id=\"T_7d699_row2_col4\" class=\"data row2 col4\" >0.0094</td>\n",
       "      <td id=\"T_7d699_row2_col5\" class=\"data row2 col5\" >0.0082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d699_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_7d699_row3_col0\" class=\"data row3 col0\" >0.0903</td>\n",
       "      <td id=\"T_7d699_row3_col1\" class=\"data row3 col1\" >0.0246</td>\n",
       "      <td id=\"T_7d699_row3_col2\" class=\"data row3 col2\" >0.1569</td>\n",
       "      <td id=\"T_7d699_row3_col3\" class=\"data row3 col3\" >0.9985</td>\n",
       "      <td id=\"T_7d699_row3_col4\" class=\"data row3 col4\" >0.0139</td>\n",
       "      <td id=\"T_7d699_row3_col5\" class=\"data row3 col5\" >0.0085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d699_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_7d699_row4_col0\" class=\"data row4 col0\" >0.0752</td>\n",
       "      <td id=\"T_7d699_row4_col1\" class=\"data row4 col1\" >0.0102</td>\n",
       "      <td id=\"T_7d699_row4_col2\" class=\"data row4 col2\" >0.1012</td>\n",
       "      <td id=\"T_7d699_row4_col3\" class=\"data row4 col3\" >0.9994</td>\n",
       "      <td id=\"T_7d699_row4_col4\" class=\"data row4 col4\" >0.0080</td>\n",
       "      <td id=\"T_7d699_row4_col5\" class=\"data row4 col5\" >0.0068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d699_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_7d699_row5_col0\" class=\"data row5 col0\" >0.0898</td>\n",
       "      <td id=\"T_7d699_row5_col1\" class=\"data row5 col1\" >0.0143</td>\n",
       "      <td id=\"T_7d699_row5_col2\" class=\"data row5 col2\" >0.1197</td>\n",
       "      <td id=\"T_7d699_row5_col3\" class=\"data row5 col3\" >0.9992</td>\n",
       "      <td id=\"T_7d699_row5_col4\" class=\"data row5 col4\" >0.0094</td>\n",
       "      <td id=\"T_7d699_row5_col5\" class=\"data row5 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d699_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_7d699_row6_col0\" class=\"data row6 col0\" >0.0870</td>\n",
       "      <td id=\"T_7d699_row6_col1\" class=\"data row6 col1\" >0.0123</td>\n",
       "      <td id=\"T_7d699_row6_col2\" class=\"data row6 col2\" >0.1110</td>\n",
       "      <td id=\"T_7d699_row6_col3\" class=\"data row6 col3\" >0.9994</td>\n",
       "      <td id=\"T_7d699_row6_col4\" class=\"data row6 col4\" >0.0091</td>\n",
       "      <td id=\"T_7d699_row6_col5\" class=\"data row6 col5\" >0.0079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d699_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_7d699_row7_col0\" class=\"data row7 col0\" >0.0841</td>\n",
       "      <td id=\"T_7d699_row7_col1\" class=\"data row7 col1\" >0.0119</td>\n",
       "      <td id=\"T_7d699_row7_col2\" class=\"data row7 col2\" >0.1092</td>\n",
       "      <td id=\"T_7d699_row7_col3\" class=\"data row7 col3\" >0.9994</td>\n",
       "      <td id=\"T_7d699_row7_col4\" class=\"data row7 col4\" >0.0103</td>\n",
       "      <td id=\"T_7d699_row7_col5\" class=\"data row7 col5\" >0.0087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d699_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_7d699_row8_col0\" class=\"data row8 col0\" >0.0824</td>\n",
       "      <td id=\"T_7d699_row8_col1\" class=\"data row8 col1\" >0.0121</td>\n",
       "      <td id=\"T_7d699_row8_col2\" class=\"data row8 col2\" >0.1102</td>\n",
       "      <td id=\"T_7d699_row8_col3\" class=\"data row8 col3\" >0.9993</td>\n",
       "      <td id=\"T_7d699_row8_col4\" class=\"data row8 col4\" >0.0095</td>\n",
       "      <td id=\"T_7d699_row8_col5\" class=\"data row8 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d699_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_7d699_row9_col0\" class=\"data row9 col0\" >0.0875</td>\n",
       "      <td id=\"T_7d699_row9_col1\" class=\"data row9 col1\" >0.0154</td>\n",
       "      <td id=\"T_7d699_row9_col2\" class=\"data row9 col2\" >0.1240</td>\n",
       "      <td id=\"T_7d699_row9_col3\" class=\"data row9 col3\" >0.9990</td>\n",
       "      <td id=\"T_7d699_row9_col4\" class=\"data row9 col4\" >0.0104</td>\n",
       "      <td id=\"T_7d699_row9_col5\" class=\"data row9 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d699_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_7d699_row10_col0\" class=\"data row10 col0\" >0.0854</td>\n",
       "      <td id=\"T_7d699_row10_col1\" class=\"data row10 col1\" >0.0141</td>\n",
       "      <td id=\"T_7d699_row10_col2\" class=\"data row10 col2\" >0.1180</td>\n",
       "      <td id=\"T_7d699_row10_col3\" class=\"data row10 col3\" >0.9992</td>\n",
       "      <td id=\"T_7d699_row10_col4\" class=\"data row10 col4\" >0.0100</td>\n",
       "      <td id=\"T_7d699_row10_col5\" class=\"data row10 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d699_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_7d699_row11_col0\" class=\"data row11 col0\" >0.0045</td>\n",
       "      <td id=\"T_7d699_row11_col1\" class=\"data row11 col1\" >0.0039</td>\n",
       "      <td id=\"T_7d699_row11_col2\" class=\"data row11 col2\" >0.0152</td>\n",
       "      <td id=\"T_7d699_row11_col3\" class=\"data row11 col3\" >0.0003</td>\n",
       "      <td id=\"T_7d699_row11_col4\" class=\"data row11 col4\" >0.0015</td>\n",
       "      <td id=\"T_7d699_row11_col5\" class=\"data row11 col5\" >0.0005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f420ccdd60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ridge = create_model('ridge', fold=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dc89074d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_ca4cb_row10_col0, #T_ca4cb_row10_col1, #T_ca4cb_row10_col2, #T_ca4cb_row10_col3, #T_ca4cb_row10_col4, #T_ca4cb_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_ca4cb\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ca4cb_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_ca4cb_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_ca4cb_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_ca4cb_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_ca4cb_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_ca4cb_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ca4cb_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_ca4cb_row0_col0\" class=\"data row0 col0\" >0.0883</td>\n",
       "      <td id=\"T_ca4cb_row0_col1\" class=\"data row0 col1\" >0.0157</td>\n",
       "      <td id=\"T_ca4cb_row0_col2\" class=\"data row0 col2\" >0.1252</td>\n",
       "      <td id=\"T_ca4cb_row0_col3\" class=\"data row0 col3\" >0.9992</td>\n",
       "      <td id=\"T_ca4cb_row0_col4\" class=\"data row0 col4\" >0.0106</td>\n",
       "      <td id=\"T_ca4cb_row0_col5\" class=\"data row0 col5\" >0.0087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca4cb_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_ca4cb_row1_col0\" class=\"data row1 col0\" >0.0882</td>\n",
       "      <td id=\"T_ca4cb_row1_col1\" class=\"data row1 col1\" >0.0154</td>\n",
       "      <td id=\"T_ca4cb_row1_col2\" class=\"data row1 col2\" >0.1240</td>\n",
       "      <td id=\"T_ca4cb_row1_col3\" class=\"data row1 col3\" >0.9991</td>\n",
       "      <td id=\"T_ca4cb_row1_col4\" class=\"data row1 col4\" >0.0098</td>\n",
       "      <td id=\"T_ca4cb_row1_col5\" class=\"data row1 col5\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca4cb_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_ca4cb_row2_col0\" class=\"data row2 col0\" >0.0803</td>\n",
       "      <td id=\"T_ca4cb_row2_col1\" class=\"data row2 col1\" >0.0104</td>\n",
       "      <td id=\"T_ca4cb_row2_col2\" class=\"data row2 col2\" >0.1021</td>\n",
       "      <td id=\"T_ca4cb_row2_col3\" class=\"data row2 col3\" >0.9994</td>\n",
       "      <td id=\"T_ca4cb_row2_col4\" class=\"data row2 col4\" >0.0094</td>\n",
       "      <td id=\"T_ca4cb_row2_col5\" class=\"data row2 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca4cb_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_ca4cb_row3_col0\" class=\"data row3 col0\" >0.0895</td>\n",
       "      <td id=\"T_ca4cb_row3_col1\" class=\"data row3 col1\" >0.0249</td>\n",
       "      <td id=\"T_ca4cb_row3_col2\" class=\"data row3 col2\" >0.1577</td>\n",
       "      <td id=\"T_ca4cb_row3_col3\" class=\"data row3 col3\" >0.9984</td>\n",
       "      <td id=\"T_ca4cb_row3_col4\" class=\"data row3 col4\" >0.0140</td>\n",
       "      <td id=\"T_ca4cb_row3_col5\" class=\"data row3 col5\" >0.0084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca4cb_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_ca4cb_row4_col0\" class=\"data row4 col0\" >0.0744</td>\n",
       "      <td id=\"T_ca4cb_row4_col1\" class=\"data row4 col1\" >0.0102</td>\n",
       "      <td id=\"T_ca4cb_row4_col2\" class=\"data row4 col2\" >0.1010</td>\n",
       "      <td id=\"T_ca4cb_row4_col3\" class=\"data row4 col3\" >0.9994</td>\n",
       "      <td id=\"T_ca4cb_row4_col4\" class=\"data row4 col4\" >0.0079</td>\n",
       "      <td id=\"T_ca4cb_row4_col5\" class=\"data row4 col5\" >0.0067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca4cb_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_ca4cb_row5_col0\" class=\"data row5 col0\" >0.0891</td>\n",
       "      <td id=\"T_ca4cb_row5_col1\" class=\"data row5 col1\" >0.0142</td>\n",
       "      <td id=\"T_ca4cb_row5_col2\" class=\"data row5 col2\" >0.1193</td>\n",
       "      <td id=\"T_ca4cb_row5_col3\" class=\"data row5 col3\" >0.9992</td>\n",
       "      <td id=\"T_ca4cb_row5_col4\" class=\"data row5 col4\" >0.0094</td>\n",
       "      <td id=\"T_ca4cb_row5_col5\" class=\"data row5 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca4cb_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_ca4cb_row6_col0\" class=\"data row6 col0\" >0.0853</td>\n",
       "      <td id=\"T_ca4cb_row6_col1\" class=\"data row6 col1\" >0.0120</td>\n",
       "      <td id=\"T_ca4cb_row6_col2\" class=\"data row6 col2\" >0.1095</td>\n",
       "      <td id=\"T_ca4cb_row6_col3\" class=\"data row6 col3\" >0.9994</td>\n",
       "      <td id=\"T_ca4cb_row6_col4\" class=\"data row6 col4\" >0.0090</td>\n",
       "      <td id=\"T_ca4cb_row6_col5\" class=\"data row6 col5\" >0.0078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca4cb_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_ca4cb_row7_col0\" class=\"data row7 col0\" >0.0835</td>\n",
       "      <td id=\"T_ca4cb_row7_col1\" class=\"data row7 col1\" >0.0121</td>\n",
       "      <td id=\"T_ca4cb_row7_col2\" class=\"data row7 col2\" >0.1100</td>\n",
       "      <td id=\"T_ca4cb_row7_col3\" class=\"data row7 col3\" >0.9994</td>\n",
       "      <td id=\"T_ca4cb_row7_col4\" class=\"data row7 col4\" >0.0104</td>\n",
       "      <td id=\"T_ca4cb_row7_col5\" class=\"data row7 col5\" >0.0087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca4cb_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_ca4cb_row8_col0\" class=\"data row8 col0\" >0.0805</td>\n",
       "      <td id=\"T_ca4cb_row8_col1\" class=\"data row8 col1\" >0.0119</td>\n",
       "      <td id=\"T_ca4cb_row8_col2\" class=\"data row8 col2\" >0.1093</td>\n",
       "      <td id=\"T_ca4cb_row8_col3\" class=\"data row8 col3\" >0.9993</td>\n",
       "      <td id=\"T_ca4cb_row8_col4\" class=\"data row8 col4\" >0.0094</td>\n",
       "      <td id=\"T_ca4cb_row8_col5\" class=\"data row8 col5\" >0.0079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca4cb_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_ca4cb_row9_col0\" class=\"data row9 col0\" >0.0859</td>\n",
       "      <td id=\"T_ca4cb_row9_col1\" class=\"data row9 col1\" >0.0152</td>\n",
       "      <td id=\"T_ca4cb_row9_col2\" class=\"data row9 col2\" >0.1232</td>\n",
       "      <td id=\"T_ca4cb_row9_col3\" class=\"data row9 col3\" >0.9990</td>\n",
       "      <td id=\"T_ca4cb_row9_col4\" class=\"data row9 col4\" >0.0104</td>\n",
       "      <td id=\"T_ca4cb_row9_col5\" class=\"data row9 col5\" >0.0079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca4cb_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_ca4cb_row10_col0\" class=\"data row10 col0\" >0.0845</td>\n",
       "      <td id=\"T_ca4cb_row10_col1\" class=\"data row10 col1\" >0.0142</td>\n",
       "      <td id=\"T_ca4cb_row10_col2\" class=\"data row10 col2\" >0.1181</td>\n",
       "      <td id=\"T_ca4cb_row10_col3\" class=\"data row10 col3\" >0.9992</td>\n",
       "      <td id=\"T_ca4cb_row10_col4\" class=\"data row10 col4\" >0.0100</td>\n",
       "      <td id=\"T_ca4cb_row10_col5\" class=\"data row10 col5\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca4cb_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_ca4cb_row11_col0\" class=\"data row11 col0\" >0.0046</td>\n",
       "      <td id=\"T_ca4cb_row11_col1\" class=\"data row11 col1\" >0.0040</td>\n",
       "      <td id=\"T_ca4cb_row11_col2\" class=\"data row11 col2\" >0.0156</td>\n",
       "      <td id=\"T_ca4cb_row11_col3\" class=\"data row11 col3\" >0.0003</td>\n",
       "      <td id=\"T_ca4cb_row11_col4\" class=\"data row11 col4\" >0.0015</td>\n",
       "      <td id=\"T_ca4cb_row11_col5\" class=\"data row11 col5\" >0.0005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f420c9e400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lar = create_model('lar', fold=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c7ba00",
   "metadata": {},
   "source": [
    "Observa-se que, testando 10 vezes cada modelo, o modelo **Bayesian Ridge** segue tendo as melhores performances diante dos demais modelos neste conjunto de dados."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a72e9ea",
   "metadata": {},
   "source": [
    "## Tunning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe1d735",
   "metadata": {},
   "source": [
    "Visando melhorar ainda mais a performance dos modelos, podemos utilizar o método *tune_model()* do PyCaret para observar se conseguimos atingir melhorias nas métricas de avaliação dos modelos ou não."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "63f7a6fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_1f368_row10_col0, #T_1f368_row10_col1, #T_1f368_row10_col2, #T_1f368_row10_col3, #T_1f368_row10_col4, #T_1f368_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_1f368\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_1f368_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_1f368_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_1f368_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_1f368_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_1f368_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_1f368_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1f368_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_1f368_row0_col0\" class=\"data row0 col0\" >0.0883</td>\n",
       "      <td id=\"T_1f368_row0_col1\" class=\"data row0 col1\" >0.0157</td>\n",
       "      <td id=\"T_1f368_row0_col2\" class=\"data row0 col2\" >0.1252</td>\n",
       "      <td id=\"T_1f368_row0_col3\" class=\"data row0 col3\" >0.9992</td>\n",
       "      <td id=\"T_1f368_row0_col4\" class=\"data row0 col4\" >0.0106</td>\n",
       "      <td id=\"T_1f368_row0_col5\" class=\"data row0 col5\" >0.0087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1f368_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_1f368_row1_col0\" class=\"data row1 col0\" >0.0880</td>\n",
       "      <td id=\"T_1f368_row1_col1\" class=\"data row1 col1\" >0.0146</td>\n",
       "      <td id=\"T_1f368_row1_col2\" class=\"data row1 col2\" >0.1209</td>\n",
       "      <td id=\"T_1f368_row1_col3\" class=\"data row1 col3\" >0.9992</td>\n",
       "      <td id=\"T_1f368_row1_col4\" class=\"data row1 col4\" >0.0096</td>\n",
       "      <td id=\"T_1f368_row1_col5\" class=\"data row1 col5\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1f368_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_1f368_row2_col0\" class=\"data row2 col0\" >0.0804</td>\n",
       "      <td id=\"T_1f368_row2_col1\" class=\"data row2 col1\" >0.0104</td>\n",
       "      <td id=\"T_1f368_row2_col2\" class=\"data row2 col2\" >0.1020</td>\n",
       "      <td id=\"T_1f368_row2_col3\" class=\"data row2 col3\" >0.9994</td>\n",
       "      <td id=\"T_1f368_row2_col4\" class=\"data row2 col4\" >0.0094</td>\n",
       "      <td id=\"T_1f368_row2_col5\" class=\"data row2 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1f368_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_1f368_row3_col0\" class=\"data row3 col0\" >0.0895</td>\n",
       "      <td id=\"T_1f368_row3_col1\" class=\"data row3 col1\" >0.0248</td>\n",
       "      <td id=\"T_1f368_row3_col2\" class=\"data row3 col2\" >0.1576</td>\n",
       "      <td id=\"T_1f368_row3_col3\" class=\"data row3 col3\" >0.9984</td>\n",
       "      <td id=\"T_1f368_row3_col4\" class=\"data row3 col4\" >0.0140</td>\n",
       "      <td id=\"T_1f368_row3_col5\" class=\"data row3 col5\" >0.0084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1f368_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_1f368_row4_col0\" class=\"data row4 col0\" >0.0744</td>\n",
       "      <td id=\"T_1f368_row4_col1\" class=\"data row4 col1\" >0.0102</td>\n",
       "      <td id=\"T_1f368_row4_col2\" class=\"data row4 col2\" >0.1010</td>\n",
       "      <td id=\"T_1f368_row4_col3\" class=\"data row4 col3\" >0.9994</td>\n",
       "      <td id=\"T_1f368_row4_col4\" class=\"data row4 col4\" >0.0079</td>\n",
       "      <td id=\"T_1f368_row4_col5\" class=\"data row4 col5\" >0.0067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1f368_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_1f368_row5_col0\" class=\"data row5 col0\" >0.0891</td>\n",
       "      <td id=\"T_1f368_row5_col1\" class=\"data row5 col1\" >0.0142</td>\n",
       "      <td id=\"T_1f368_row5_col2\" class=\"data row5 col2\" >0.1193</td>\n",
       "      <td id=\"T_1f368_row5_col3\" class=\"data row5 col3\" >0.9992</td>\n",
       "      <td id=\"T_1f368_row5_col4\" class=\"data row5 col4\" >0.0094</td>\n",
       "      <td id=\"T_1f368_row5_col5\" class=\"data row5 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1f368_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_1f368_row6_col0\" class=\"data row6 col0\" >0.0853</td>\n",
       "      <td id=\"T_1f368_row6_col1\" class=\"data row6 col1\" >0.0120</td>\n",
       "      <td id=\"T_1f368_row6_col2\" class=\"data row6 col2\" >0.1095</td>\n",
       "      <td id=\"T_1f368_row6_col3\" class=\"data row6 col3\" >0.9994</td>\n",
       "      <td id=\"T_1f368_row6_col4\" class=\"data row6 col4\" >0.0090</td>\n",
       "      <td id=\"T_1f368_row6_col5\" class=\"data row6 col5\" >0.0078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1f368_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_1f368_row7_col0\" class=\"data row7 col0\" >0.0835</td>\n",
       "      <td id=\"T_1f368_row7_col1\" class=\"data row7 col1\" >0.0121</td>\n",
       "      <td id=\"T_1f368_row7_col2\" class=\"data row7 col2\" >0.1099</td>\n",
       "      <td id=\"T_1f368_row7_col3\" class=\"data row7 col3\" >0.9994</td>\n",
       "      <td id=\"T_1f368_row7_col4\" class=\"data row7 col4\" >0.0104</td>\n",
       "      <td id=\"T_1f368_row7_col5\" class=\"data row7 col5\" >0.0087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1f368_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_1f368_row8_col0\" class=\"data row8 col0\" >0.0814</td>\n",
       "      <td id=\"T_1f368_row8_col1\" class=\"data row8 col1\" >0.0120</td>\n",
       "      <td id=\"T_1f368_row8_col2\" class=\"data row8 col2\" >0.1094</td>\n",
       "      <td id=\"T_1f368_row8_col3\" class=\"data row8 col3\" >0.9993</td>\n",
       "      <td id=\"T_1f368_row8_col4\" class=\"data row8 col4\" >0.0094</td>\n",
       "      <td id=\"T_1f368_row8_col5\" class=\"data row8 col5\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1f368_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_1f368_row9_col0\" class=\"data row9 col0\" >0.0859</td>\n",
       "      <td id=\"T_1f368_row9_col1\" class=\"data row9 col1\" >0.0152</td>\n",
       "      <td id=\"T_1f368_row9_col2\" class=\"data row9 col2\" >0.1232</td>\n",
       "      <td id=\"T_1f368_row9_col3\" class=\"data row9 col3\" >0.9990</td>\n",
       "      <td id=\"T_1f368_row9_col4\" class=\"data row9 col4\" >0.0104</td>\n",
       "      <td id=\"T_1f368_row9_col5\" class=\"data row9 col5\" >0.0079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1f368_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_1f368_row10_col0\" class=\"data row10 col0\" >0.0846</td>\n",
       "      <td id=\"T_1f368_row10_col1\" class=\"data row10 col1\" >0.0141</td>\n",
       "      <td id=\"T_1f368_row10_col2\" class=\"data row10 col2\" >0.1178</td>\n",
       "      <td id=\"T_1f368_row10_col3\" class=\"data row10 col3\" >0.9992</td>\n",
       "      <td id=\"T_1f368_row10_col4\" class=\"data row10 col4\" >0.0100</td>\n",
       "      <td id=\"T_1f368_row10_col5\" class=\"data row10 col5\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1f368_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_1f368_row11_col0\" class=\"data row11 col0\" >0.0045</td>\n",
       "      <td id=\"T_1f368_row11_col1\" class=\"data row11 col1\" >0.0040</td>\n",
       "      <td id=\"T_1f368_row11_col2\" class=\"data row11 col2\" >0.0155</td>\n",
       "      <td id=\"T_1f368_row11_col3\" class=\"data row11 col3\" >0.0003</td>\n",
       "      <td id=\"T_1f368_row11_col4\" class=\"data row11 col4\" >0.0015</td>\n",
       "      <td id=\"T_1f368_row11_col5\" class=\"data row11 col5\" >0.0005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f420c914f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {'alpha': [0.02, 0.024, 0.025, 0.026, 0.03]}\n",
    "tune_ridge = tune_model(ridge, n_iter=1000, optimize = 'RMSE', custom_grid = params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "066d99d0",
   "metadata": {},
   "source": [
    "Houve melhorias na média do MAE e no RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4c4b65fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7e8e3_row10_col0, #T_7e8e3_row10_col1, #T_7e8e3_row10_col2, #T_7e8e3_row10_col3, #T_7e8e3_row10_col4, #T_7e8e3_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7e8e3\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7e8e3_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_7e8e3_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_7e8e3_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_7e8e3_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_7e8e3_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_7e8e3_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7e8e3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7e8e3_row0_col0\" class=\"data row0 col0\" >0.0883</td>\n",
       "      <td id=\"T_7e8e3_row0_col1\" class=\"data row0 col1\" >0.0157</td>\n",
       "      <td id=\"T_7e8e3_row0_col2\" class=\"data row0 col2\" >0.1252</td>\n",
       "      <td id=\"T_7e8e3_row0_col3\" class=\"data row0 col3\" >0.9992</td>\n",
       "      <td id=\"T_7e8e3_row0_col4\" class=\"data row0 col4\" >0.0106</td>\n",
       "      <td id=\"T_7e8e3_row0_col5\" class=\"data row0 col5\" >0.0087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7e8e3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_7e8e3_row1_col0\" class=\"data row1 col0\" >0.0882</td>\n",
       "      <td id=\"T_7e8e3_row1_col1\" class=\"data row1 col1\" >0.0154</td>\n",
       "      <td id=\"T_7e8e3_row1_col2\" class=\"data row1 col2\" >0.1240</td>\n",
       "      <td id=\"T_7e8e3_row1_col3\" class=\"data row1 col3\" >0.9991</td>\n",
       "      <td id=\"T_7e8e3_row1_col4\" class=\"data row1 col4\" >0.0098</td>\n",
       "      <td id=\"T_7e8e3_row1_col5\" class=\"data row1 col5\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7e8e3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_7e8e3_row2_col0\" class=\"data row2 col0\" >0.0803</td>\n",
       "      <td id=\"T_7e8e3_row2_col1\" class=\"data row2 col1\" >0.0104</td>\n",
       "      <td id=\"T_7e8e3_row2_col2\" class=\"data row2 col2\" >0.1021</td>\n",
       "      <td id=\"T_7e8e3_row2_col3\" class=\"data row2 col3\" >0.9994</td>\n",
       "      <td id=\"T_7e8e3_row2_col4\" class=\"data row2 col4\" >0.0094</td>\n",
       "      <td id=\"T_7e8e3_row2_col5\" class=\"data row2 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7e8e3_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_7e8e3_row3_col0\" class=\"data row3 col0\" >0.0895</td>\n",
       "      <td id=\"T_7e8e3_row3_col1\" class=\"data row3 col1\" >0.0249</td>\n",
       "      <td id=\"T_7e8e3_row3_col2\" class=\"data row3 col2\" >0.1577</td>\n",
       "      <td id=\"T_7e8e3_row3_col3\" class=\"data row3 col3\" >0.9984</td>\n",
       "      <td id=\"T_7e8e3_row3_col4\" class=\"data row3 col4\" >0.0140</td>\n",
       "      <td id=\"T_7e8e3_row3_col5\" class=\"data row3 col5\" >0.0084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7e8e3_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_7e8e3_row4_col0\" class=\"data row4 col0\" >0.0744</td>\n",
       "      <td id=\"T_7e8e3_row4_col1\" class=\"data row4 col1\" >0.0102</td>\n",
       "      <td id=\"T_7e8e3_row4_col2\" class=\"data row4 col2\" >0.1010</td>\n",
       "      <td id=\"T_7e8e3_row4_col3\" class=\"data row4 col3\" >0.9994</td>\n",
       "      <td id=\"T_7e8e3_row4_col4\" class=\"data row4 col4\" >0.0079</td>\n",
       "      <td id=\"T_7e8e3_row4_col5\" class=\"data row4 col5\" >0.0067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7e8e3_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_7e8e3_row5_col0\" class=\"data row5 col0\" >0.0891</td>\n",
       "      <td id=\"T_7e8e3_row5_col1\" class=\"data row5 col1\" >0.0142</td>\n",
       "      <td id=\"T_7e8e3_row5_col2\" class=\"data row5 col2\" >0.1193</td>\n",
       "      <td id=\"T_7e8e3_row5_col3\" class=\"data row5 col3\" >0.9992</td>\n",
       "      <td id=\"T_7e8e3_row5_col4\" class=\"data row5 col4\" >0.0094</td>\n",
       "      <td id=\"T_7e8e3_row5_col5\" class=\"data row5 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7e8e3_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_7e8e3_row6_col0\" class=\"data row6 col0\" >0.0853</td>\n",
       "      <td id=\"T_7e8e3_row6_col1\" class=\"data row6 col1\" >0.0120</td>\n",
       "      <td id=\"T_7e8e3_row6_col2\" class=\"data row6 col2\" >0.1095</td>\n",
       "      <td id=\"T_7e8e3_row6_col3\" class=\"data row6 col3\" >0.9994</td>\n",
       "      <td id=\"T_7e8e3_row6_col4\" class=\"data row6 col4\" >0.0090</td>\n",
       "      <td id=\"T_7e8e3_row6_col5\" class=\"data row6 col5\" >0.0078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7e8e3_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_7e8e3_row7_col0\" class=\"data row7 col0\" >0.0835</td>\n",
       "      <td id=\"T_7e8e3_row7_col1\" class=\"data row7 col1\" >0.0121</td>\n",
       "      <td id=\"T_7e8e3_row7_col2\" class=\"data row7 col2\" >0.1100</td>\n",
       "      <td id=\"T_7e8e3_row7_col3\" class=\"data row7 col3\" >0.9994</td>\n",
       "      <td id=\"T_7e8e3_row7_col4\" class=\"data row7 col4\" >0.0104</td>\n",
       "      <td id=\"T_7e8e3_row7_col5\" class=\"data row7 col5\" >0.0087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7e8e3_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_7e8e3_row8_col0\" class=\"data row8 col0\" >0.0805</td>\n",
       "      <td id=\"T_7e8e3_row8_col1\" class=\"data row8 col1\" >0.0119</td>\n",
       "      <td id=\"T_7e8e3_row8_col2\" class=\"data row8 col2\" >0.1093</td>\n",
       "      <td id=\"T_7e8e3_row8_col3\" class=\"data row8 col3\" >0.9993</td>\n",
       "      <td id=\"T_7e8e3_row8_col4\" class=\"data row8 col4\" >0.0094</td>\n",
       "      <td id=\"T_7e8e3_row8_col5\" class=\"data row8 col5\" >0.0079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7e8e3_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_7e8e3_row9_col0\" class=\"data row9 col0\" >0.0859</td>\n",
       "      <td id=\"T_7e8e3_row9_col1\" class=\"data row9 col1\" >0.0152</td>\n",
       "      <td id=\"T_7e8e3_row9_col2\" class=\"data row9 col2\" >0.1232</td>\n",
       "      <td id=\"T_7e8e3_row9_col3\" class=\"data row9 col3\" >0.9990</td>\n",
       "      <td id=\"T_7e8e3_row9_col4\" class=\"data row9 col4\" >0.0104</td>\n",
       "      <td id=\"T_7e8e3_row9_col5\" class=\"data row9 col5\" >0.0079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7e8e3_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_7e8e3_row10_col0\" class=\"data row10 col0\" >0.0845</td>\n",
       "      <td id=\"T_7e8e3_row10_col1\" class=\"data row10 col1\" >0.0142</td>\n",
       "      <td id=\"T_7e8e3_row10_col2\" class=\"data row10 col2\" >0.1181</td>\n",
       "      <td id=\"T_7e8e3_row10_col3\" class=\"data row10 col3\" >0.9992</td>\n",
       "      <td id=\"T_7e8e3_row10_col4\" class=\"data row10 col4\" >0.0100</td>\n",
       "      <td id=\"T_7e8e3_row10_col5\" class=\"data row10 col5\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7e8e3_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_7e8e3_row11_col0\" class=\"data row11 col0\" >0.0046</td>\n",
       "      <td id=\"T_7e8e3_row11_col1\" class=\"data row11 col1\" >0.0040</td>\n",
       "      <td id=\"T_7e8e3_row11_col2\" class=\"data row11 col2\" >0.0156</td>\n",
       "      <td id=\"T_7e8e3_row11_col3\" class=\"data row11 col3\" >0.0003</td>\n",
       "      <td id=\"T_7e8e3_row11_col4\" class=\"data row11 col4\" >0.0015</td>\n",
       "      <td id=\"T_7e8e3_row11_col5\" class=\"data row11 col5\" >0.0005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f4249045e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tune_lar = tune_model(lar, n_iter=1000, optimize = 'RMSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "357a3cda",
   "metadata": {},
   "source": [
    "Houve piora no MAE, melhora no MSE e no RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b5df6b5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_5eed1_row10_col0, #T_5eed1_row10_col1, #T_5eed1_row10_col2, #T_5eed1_row10_col3, #T_5eed1_row10_col4, #T_5eed1_row10_col5 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_5eed1\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_5eed1_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
       "      <th id=\"T_5eed1_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
       "      <th id=\"T_5eed1_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
       "      <th id=\"T_5eed1_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
       "      <th id=\"T_5eed1_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
       "      <th id=\"T_5eed1_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5eed1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_5eed1_row0_col0\" class=\"data row0 col0\" >0.0883</td>\n",
       "      <td id=\"T_5eed1_row0_col1\" class=\"data row0 col1\" >0.0157</td>\n",
       "      <td id=\"T_5eed1_row0_col2\" class=\"data row0 col2\" >0.1251</td>\n",
       "      <td id=\"T_5eed1_row0_col3\" class=\"data row0 col3\" >0.9992</td>\n",
       "      <td id=\"T_5eed1_row0_col4\" class=\"data row0 col4\" >0.0106</td>\n",
       "      <td id=\"T_5eed1_row0_col5\" class=\"data row0 col5\" >0.0087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5eed1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_5eed1_row1_col0\" class=\"data row1 col0\" >0.0876</td>\n",
       "      <td id=\"T_5eed1_row1_col1\" class=\"data row1 col1\" >0.0145</td>\n",
       "      <td id=\"T_5eed1_row1_col2\" class=\"data row1 col2\" >0.1204</td>\n",
       "      <td id=\"T_5eed1_row1_col3\" class=\"data row1 col3\" >0.9992</td>\n",
       "      <td id=\"T_5eed1_row1_col4\" class=\"data row1 col4\" >0.0095</td>\n",
       "      <td id=\"T_5eed1_row1_col5\" class=\"data row1 col5\" >0.0079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5eed1_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_5eed1_row2_col0\" class=\"data row2 col0\" >0.0803</td>\n",
       "      <td id=\"T_5eed1_row2_col1\" class=\"data row2 col1\" >0.0104</td>\n",
       "      <td id=\"T_5eed1_row2_col2\" class=\"data row2 col2\" >0.1020</td>\n",
       "      <td id=\"T_5eed1_row2_col3\" class=\"data row2 col3\" >0.9994</td>\n",
       "      <td id=\"T_5eed1_row2_col4\" class=\"data row2 col4\" >0.0094</td>\n",
       "      <td id=\"T_5eed1_row2_col5\" class=\"data row2 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5eed1_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_5eed1_row3_col0\" class=\"data row3 col0\" >0.0896</td>\n",
       "      <td id=\"T_5eed1_row3_col1\" class=\"data row3 col1\" >0.0248</td>\n",
       "      <td id=\"T_5eed1_row3_col2\" class=\"data row3 col2\" >0.1575</td>\n",
       "      <td id=\"T_5eed1_row3_col3\" class=\"data row3 col3\" >0.9984</td>\n",
       "      <td id=\"T_5eed1_row3_col4\" class=\"data row3 col4\" >0.0140</td>\n",
       "      <td id=\"T_5eed1_row3_col5\" class=\"data row3 col5\" >0.0084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5eed1_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_5eed1_row4_col0\" class=\"data row4 col0\" >0.0744</td>\n",
       "      <td id=\"T_5eed1_row4_col1\" class=\"data row4 col1\" >0.0102</td>\n",
       "      <td id=\"T_5eed1_row4_col2\" class=\"data row4 col2\" >0.1010</td>\n",
       "      <td id=\"T_5eed1_row4_col3\" class=\"data row4 col3\" >0.9994</td>\n",
       "      <td id=\"T_5eed1_row4_col4\" class=\"data row4 col4\" >0.0079</td>\n",
       "      <td id=\"T_5eed1_row4_col5\" class=\"data row4 col5\" >0.0067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5eed1_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_5eed1_row5_col0\" class=\"data row5 col0\" >0.0892</td>\n",
       "      <td id=\"T_5eed1_row5_col1\" class=\"data row5 col1\" >0.0142</td>\n",
       "      <td id=\"T_5eed1_row5_col2\" class=\"data row5 col2\" >0.1193</td>\n",
       "      <td id=\"T_5eed1_row5_col3\" class=\"data row5 col3\" >0.9992</td>\n",
       "      <td id=\"T_5eed1_row5_col4\" class=\"data row5 col4\" >0.0094</td>\n",
       "      <td id=\"T_5eed1_row5_col5\" class=\"data row5 col5\" >0.0081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5eed1_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_5eed1_row6_col0\" class=\"data row6 col0\" >0.0853</td>\n",
       "      <td id=\"T_5eed1_row6_col1\" class=\"data row6 col1\" >0.0120</td>\n",
       "      <td id=\"T_5eed1_row6_col2\" class=\"data row6 col2\" >0.1095</td>\n",
       "      <td id=\"T_5eed1_row6_col3\" class=\"data row6 col3\" >0.9994</td>\n",
       "      <td id=\"T_5eed1_row6_col4\" class=\"data row6 col4\" >0.0090</td>\n",
       "      <td id=\"T_5eed1_row6_col5\" class=\"data row6 col5\" >0.0078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5eed1_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_5eed1_row7_col0\" class=\"data row7 col0\" >0.0835</td>\n",
       "      <td id=\"T_5eed1_row7_col1\" class=\"data row7 col1\" >0.0121</td>\n",
       "      <td id=\"T_5eed1_row7_col2\" class=\"data row7 col2\" >0.1099</td>\n",
       "      <td id=\"T_5eed1_row7_col3\" class=\"data row7 col3\" >0.9994</td>\n",
       "      <td id=\"T_5eed1_row7_col4\" class=\"data row7 col4\" >0.0104</td>\n",
       "      <td id=\"T_5eed1_row7_col5\" class=\"data row7 col5\" >0.0087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5eed1_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_5eed1_row8_col0\" class=\"data row8 col0\" >0.0813</td>\n",
       "      <td id=\"T_5eed1_row8_col1\" class=\"data row8 col1\" >0.0119</td>\n",
       "      <td id=\"T_5eed1_row8_col2\" class=\"data row8 col2\" >0.1091</td>\n",
       "      <td id=\"T_5eed1_row8_col3\" class=\"data row8 col3\" >0.9993</td>\n",
       "      <td id=\"T_5eed1_row8_col4\" class=\"data row8 col4\" >0.0094</td>\n",
       "      <td id=\"T_5eed1_row8_col5\" class=\"data row8 col5\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5eed1_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_5eed1_row9_col0\" class=\"data row9 col0\" >0.0859</td>\n",
       "      <td id=\"T_5eed1_row9_col1\" class=\"data row9 col1\" >0.0152</td>\n",
       "      <td id=\"T_5eed1_row9_col2\" class=\"data row9 col2\" >0.1232</td>\n",
       "      <td id=\"T_5eed1_row9_col3\" class=\"data row9 col3\" >0.9990</td>\n",
       "      <td id=\"T_5eed1_row9_col4\" class=\"data row9 col4\" >0.0104</td>\n",
       "      <td id=\"T_5eed1_row9_col5\" class=\"data row9 col5\" >0.0079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5eed1_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_5eed1_row10_col0\" class=\"data row10 col0\" >0.0845</td>\n",
       "      <td id=\"T_5eed1_row10_col1\" class=\"data row10 col1\" >0.0141</td>\n",
       "      <td id=\"T_5eed1_row10_col2\" class=\"data row10 col2\" >0.1177</td>\n",
       "      <td id=\"T_5eed1_row10_col3\" class=\"data row10 col3\" >0.9992</td>\n",
       "      <td id=\"T_5eed1_row10_col4\" class=\"data row10 col4\" >0.0100</td>\n",
       "      <td id=\"T_5eed1_row10_col5\" class=\"data row10 col5\" >0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5eed1_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_5eed1_row11_col0\" class=\"data row11 col0\" >0.0045</td>\n",
       "      <td id=\"T_5eed1_row11_col1\" class=\"data row11 col1\" >0.0040</td>\n",
       "      <td id=\"T_5eed1_row11_col2\" class=\"data row11 col2\" >0.0155</td>\n",
       "      <td id=\"T_5eed1_row11_col3\" class=\"data row11 col3\" >0.0003</td>\n",
       "      <td id=\"T_5eed1_row11_col4\" class=\"data row11 col4\" >0.0015</td>\n",
       "      <td id=\"T_5eed1_row11_col5\" class=\"data row11 col5\" >0.0005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f42495e2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tune_br = tune_model(br, n_iter=1000, optimize = 'RMSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9916e00e",
   "metadata": {},
   "source": [
    "Houve melhoria no RMSE e no MAE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf5e5989",
   "metadata": {},
   "source": [
    "## Análise gráfica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "48e046bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W5ddffyU2NrZMnrPaTLO7WE6Pl/QcB07Pha9+XJrdu3fTqFEjAOrWrev/dFfTNF566SU6d+5Mq1atLuqxt2zZQoMGDahXrx4mk4mbbrqJlStXFjsuLS2Nyy+/nPDwcAwGA1dddRXLly8vdfvZ7iNEVef0eFmdloGuyAylQ9kOTuY5OLL0DVzpaYQ3bcehpDa8vzm9zJ5XzqBLUdqfMw93boG+hL6tF6IwoDVNY9GiRUyYMAGAhQsXsm7dOvLy8jhw4AA333xzwP2GDx+O3W4v9niPP/44HTt2BCAjIyPg+v7ExES2bNlS7D7NmjXjtddeIysri7CwMNasWUNKSgrdu3cvcXvR+2RnZ6PT6QL2CVGVZdqdZNqdGPU63F4VvU4hMzsbVn6Atn8LRw7uoNWjr2Ewh7M6LYMHrr8sYHz6YklAl+LMP2dsTg9fpR4CYELXizu7BThy5Ah2u5377ruPjIwMmjdvzkMPPQTAiBEjGDFihP/YM8P4448/Pufjl7TEZEnzkpOTkxk9ejQjR44kIiKC5s2bo9frS91e9D4PPPCAf25n4T4hqrKYcCPZBS4y8gpwe1QUpwP169fQZx4Go5nGd0/CYIkC4KTDF+a1o0u+avlCSECXoKQ/ZwB0inLJvx137drFlVdeyYcffkhOTg79+vXjjz/+oF27due87/mcQSclJXH06FH/voyMDGrWrFni4w0dOpShQ4cC8Morr5CYmHjW7YX7+vbti8ViKbZPiKrqrZ93U+D24nB5cDlshH83F8PJw2g6A/q+Y7HWb+o/Ni7CTLylbNqTSkCXoPDPmZJC+FJ/O+7evZuWLVsCEB0dTb9+/fjpp5/OK6DP5wy6devW7N+/n4MHD5KYmMg333zDyy+/XOKxmZmZxMfHk56ezg8//MCnn3561u2F+8LCwkrcJ0RV5PKqrE7LQFEUPPn2gHC29xqLoWYyh7Lt1IuxoGoaXZMTy2R4AySgSxRv8f0GtDk9xfZd6m/HXbt2BXSy6t69O9OnT/ePQ18qg8HAM888w+jRo/F6vQwePJimTU//dr/33nuZNm0aiYmJPPTQQ2RnZ2MwGHj22WeJjo4GKHV74b6TJ09iMpmK7ROiKspxejluc5KVm4cx9Sf0ucfRdAYcvcai1mqCV1XJtDtpViOKG5om8XDnFmX23BUe0G63m8mTJ3P48GFcLhdjx46lSZMmPPHEEyiKQtOmTXn22WcDFlBUVZUpU6b4e6lOmzaNBg0alFuNZoOersmJ/jFofx1l8NvxzLPZq666in//+98X/Xgl6dKlC126dClx34IFC/xfl3ZGfrYz9Y8//hi73X7BTV+EqKyizXosBgXP9nWgabhTuuKtfRnUaoIeDbNeT/3YCOYMuppG8WXbZrfCA/qrr74iJiaGl156iaysLG655RYuu+wyxo8fzzXXXMMzzzzDypUr6dmzp/8+K1aswOVy8emnn7Jp0yaef/555s+fX651Fv4WXJ2WwUmHk7iI07M4hBBVW+H0WpdX5WhuPg0dB/m9Rl3cXhVP06vBaEbTNIw6HWajnjrRljL5UPBMFR7Qffr0oXfv3v7ber2e1NRUrr76agA6d+7M2rVrAwJ648aN/qty2rZty7Zt28q9Tr1Ox4SurXjg+stKvKxTCFH1eFWV137aznu/prHvpA2Px03Yqg/Q799MdO+7cba8DlVT0OELZ5NeITrMyA1Nk8olHyr8QhWLxYLVasVms/Hwww8zfvx4NE3zTwWzWCzk5eUF3Mdms2G1Wv239Xo9Hk/x8eHyYDboqR0dIeEsRDXwyqpU5vxvF39m5uF22An7+lX0+zejmcLREurStVkdkuOsWE1Gwk166sRaGNuxWbn9ZR2UDwmPHDnCgw8+yPDhw+nfvz8vvfSSf5/dbicqKirgeKvVGjC97Hyb7595pp2cnIzb7S71+JKmsAVbKNYEZ6/L7XaTlpZWgdWctnHjxqA879mEYk0gdRXlVTU+2nGCj3ZkkuNSUZ0OrMvmoj95GE1vJL/3WLAmkZOTw1vd6pDr8oIGCRFGTHonm/74o1zqqvCAPnHiBCNHjuSZZ56hQ4cOALRs2ZL169dzzTXXsGbNGq699tqA+7Rr145Vq1bRt29fNm3aRLNmzc7ruVJSUvzLpRcuclraUk2h+MFXKNYE567L5XLRunXrCl+ua+PGjbRv375Cn/NcQrEmkLoKFXame3f9HlZnuHChgNOBddkc9CfTfeHcawxqzYZo6HDpTTRt2brMxpudTudZh2wrPKDffPNNcnNzmTdvHvPmzQPgySefZNq0abzyyis0btzYP0Y9ceJExo8fT8+ePVm7di233347mqYxY8aMii5bCFGFFLZy+HHPEdbuO07uqSm1mttJxIoF/nC29xwDtZqgAEaDjhrW8DK7COV8VHhAP/XUUzz11FPFti9atKjYthdffNH/9XPPPVeudQkhqo/CVg7bjmSR6/SgAbidmPb+ihpXGzX3OI6u9+Ct1QQdYFDK98PA0kg3uwq0dOlSZs2adUmPUdIvsoMHDzJgwAAef/xxpk+fTnp6OtnZ2Xz99deX9FxCVEWFrRxUTSPX6UEB0DR0ucdRCmyoMUnkDXoSby1fi16jAo3jreX6YWBp5ErCSmb+/PnceeedAdt+//13OnTowBNPPOHftn79en788Uf69+9f0SUKEdIKWzm4PF5UVUNTvUSsfh9PUlPc9VujRtUAo5lEq5m7r0ymY6STXtdfE5SZXNX6DDouLs7/r169egG333//ff9x77//fsC+M/9diE2bNnH33XczePBgVq9eDcCvv/7KsGHDuPPOO5k0aRJut5t9+/Zxzz33cOedd3L33XeTkZHB/PnzycnJYcqUKf7HS09PZ/78+SxbtoyPP/6Yu+66i7S0NN58801++eUX6ZUhxBniLWZiwk3odAqKy0H4incwHtiC+Y/v0CwxYPSNMXs1mNwzhdqRwbsGoloHdDCEh4fz/vvv8/bbb/Pcc8/h9Xp5+umnmTt3LosWLSIxMZEvvviCn3/+mRYtWvDee+8xZswYcnJyGDt2LNHR0QEBXbt2be677z769evH8OHD/dvHjBnDtddey2233RaEVylEaHK43MxcsYW0E7ls359O+LdzMB7ejmYMw977AbQw3/UWBp2C0+Pl5dU7glpvtR7iOHnypP/rs00du+eee8ps7b327dujKArx8fFERkaSlZXFsWPHGD9+PAAFBQVcd911jB07ljfeeIPRo0cTGRlZZs2UhKiOCmdtLFi3m7RMG2qBnYjv5qLP8s3WsPW4DzWhHgqg1ylYTAYMOoUNBzPpHRd1zscvL9U6oINh69atABw/fhyHw0FsbCxJSUnMmzePyMhIVq5cSUREBCtXruSKK67g0Ucf5T//+Q/vvPMOM2fOLLEhf0l0Oh2qqpbnSxGi0njtp+3MW7uL/VkOKLD7LkIpDOdeY1GTktEpCia9QphBjwbEhJvIKXCR4yy75e4ulAR0BSsoKGDEiBE4HA6ee+459Ho9Tz75JPfddx+apmGxWHjxxRex2+08+uijLFiwAJ1Ox6RJkwDf1ZCPPfbYOWeD1K9fn927d/P+++/LytuiWnN6vLz3WxrpOfm+qXQ7/4su70RAOAMogEfV0OkUYsNN1I2xEGk2EG0OXpsHCegKNGjQIAYNGlRs+/XXX8/1118fsC0+Pp4PPvig2LDLwoULS3zckvZ/9913l1qyEJVO4dWBhQ3O0nMcHMnJR1W9mPb+iqJpOFt1w127eUA4hxt1vpbHCVGEG/X+9sImfUHQXosEtBCiSigcZ1655yhH8/KJCTdxQ9MkOjdOxOvxoPy1HW98PfSZB3E1udo/WwPApFeoYQ3D7vKgKGA1G/zthcurz8b5kIAWQlQJr63Zwfy1u8jIK8DlVVE1jdV7M4jUa7D8XSIObCW/w1BczTuCLnDYonZ0BPViLPS9rDZ3XJkcMu2FJaCFEJVeboGLBev2cCQ3H7dXQ0VD04ACO94f5mHIPIRqisCT0KBYOBt00LxGFD2a1eLhzi3Q60Jn9rEEtBCi0ioc1vhm+2HSTuQRMG+pwI71uznos4+g6U2+ec4J9dArCihg0utItIYRH2Fm7pBraBRXtstVlQUJaCFEpVXY9Mjp8RIwAbVIOKt6I/ZeY1Bq1EOnKKQkxWA26DEbdOgUBavZQO2osl+uqiyEzrm8EEJcgMKmR+Bb0Nkf0G4nESsXFAnnsWhJyRh0CpqmYTbqCTfq0SlKmSwEXZ4koCvYmjVrivXHuPXWWzl06NAFPY7T6WTx4sWAr0veypUrgZK73QlRFR3LK2BrehZbj2Sx6XCWb6O/ZWhd1PAo7L0f8E+lA0iIMBMXbsLl9WI1G7i5Vd2QXghahjgqWOfOncvkcY4fP87ixYsZOnRowDzokrrdCVHVOD1e3v5lN7lON/kuLx5VA01Fl3fiVMvQRPLaTwZTOAA6BXSKwiNdWjC+S8tKsxB0tQ7o559/3v+12+3GaDT6b/fp04e2bdsCvg50y5YtK/Vxirb5PJelS5fy559/otfr+e9//0tSUhJZWb7f/nl5eTz55JP+23//+99p27YtvXr1ol27duzbt4/4+HjmzJnDm2++yd69e5k7dy6appGQkEB2dra/211eXh79+/ena9eupKWl8cILL/D2229fyNsjRMgpOtd53f7jOD1eXF4VzeshYtV7eGo1wd2gDWpkQsA85+gwE1FhBoa3a+xfCLoykCGOIPjrr7/47bffWLJkif+ybvAtB3bttdeycOFCpk6dysyZMwFfQ/5HHnmETz/9lJMnT7J161bGjBlDkyZNGDdunP9xi3a7Gzp0KF988QUAS5YsYciQIRX/QoUoI06Pl/QcB6+s3s5XqYfIznfh1cCg1/kaH61YgPHgNsybf0CLiA4I5zCDjqY1IkmpFUvNyLAgvooLV63PoIue+Z6tm13btm39Z9NlYdu2bfTo0QOdTofVavUvgrt7925++eUX/yXaubm5AMTGxlKrVi0AatWqhdPpPOdzXHPNNUyfPp3MzEzWrl3Lo48+Wmb1C1FRCs+YV6dlcNxWwL6TNqwmAwnWMAw68NjzTs/WMIVj7/2gv2Uo+IY2wo16jHpdSH8YWJpqHdDB0qhRI7Zs2YKqqhQUFLB3714AGjduzM0330z//v3JzMzko48+AkBRlGKPUVq3usJud4qi0L9/f6ZPn851110XMHwjRGVROI1OpygoQHa+mxM2Jwez7aj5dsK/PRXOBiP2HvehxtcNuL9eUUiKCmdgSr2Q/jCwNBLQQdCiRQsSEhIYMmQINWvWJD4+HvA12X/yySf57LPPsNls3HvvvaU+Rnx8PG63m5deeomwsNN/thXtdjdo0CC6du3Kl19+We6vSYiyVjiNTnfqBOVIjgOX13dSojqKzHM2GLH3egA1sXHA/cP1Co90bcmTPVoTYaqcJygS0BWs6IyLktqAzps3z/914dj02rVr/dteffVV/9clBW/RbnZer5f27duTnJxc7DghQl3h2oE6BfadtJGee6qrXGHLUNvJYuGsAyLMBtA0Hr7+Mqb2bRe8F1AGJKCrqO+//565c+cyffr0YJcixEWJNOtJy8zjhM15+hLuoi1DU7rhrnMZas1G/vuogEFRqB0dwaSerYNRdpmSgK6ievfuTe/evYNdhhAXbfjC/5HtcJ0OZ68Hw8FUvAn10Z/4q1jLUPCdQSfXiGRY24aVdlijKAloUeY0TSvxg00hzodXVXlx5TZW7DmCSz11AbfXQ8SP72I8tJ38awbjuuy6Yl3pABQFbqmkHwiWpNoEtE6nw+VyYTKZgl1Klef1euV9FhfttTU7mPfzLlzeU+FcYMeybC6GrHRUswVPUnKxcNYBRr1CYmQYd1/VJKRahl6KahPQBoOB/Px8HA4Her2+2Bme2+3G5XIFqbqShWJNUHpdmqbh9Xrxer0YDNXmR0uUoXy3lxdXbuOE49TPV4Ed63ez0WcfRTWYsPd5EDWuTsB99EB0hInYcBOX1Ywm3mIu/sCVVNX4NXOeIiMjMZlMJf75nZaWFoSKzi4Ua4LS61IUBZPJRGRk6PXVFaEvI9fOjZ/vKj2cez8QEM4KEG7Q075ePClJMdSJjqBbk8p3McrZVLvTnLOd2YXin+WhWBOEbl2i8nF5PAz9YA3/2X749Ea3E8vKBYHhXGS2RphBR1JkOOEmPQa9QlSY0b+GYFVS7QJaCBFahr7/E9/uSD+94VTLUG98PXS2LOzd7ikWzt2bJNG9aRL3d2xGdr67UnSmuxhBC+jNmzcza9YsFi5cyIQJEzhx4gQAhw8f5vLLLw+4IANg4MCB/j+d69at628kJISovI7b8lm552iRec4qurxMX8vQ6Jrk3TIJTKevlH34uqY82i2FmpHh/kCuCtPpShOUgF6wYAFfffUV4eG+Xq2FYZyTk8OIESOYNGlSwPGFzYGKXiUnhKi8CpsgLd3yF/meU/HscRPx47t4ajXF3eBy1Mj4gHnOybEWnr/5qip5plyaoHxIWL9+febMmVNs+5w5c7jzzjupWbNmwPadO3eSn5/PyJEjGTFiBJs2baqgSoUQZaGwXajT4wV8TZC+TD1EakaO74B8GxHL38J4eAfmrSvRIqICwtms1zHq2ibVKpwBFK2w/VkFO3ToEI8++iifffYZAJmZmYwYMYKvvvoKvT7wm7Br1y42b97M0KFD2b9/P/feey/Lli0r9QM/p9PJtm3byv01CCHOzqtqfLLrJBsz7OS4PESbDFxeI4I/jtnZlplPrkuFfJtvtkZOBqopAvuN44pNpbuuloVZXeqj11XNC6BSUlIwm4tPDwyZDwmXLVtGv379ioUz+NpzNmjQAEVRaNSoETExMRw/ftzfI7k0pb3okmzcuJH27dtfVO3lJRRrAqnrQoRiTVBxdb26OpU/cjQM4RbifSOabMjysPOkq3g4G0zYe95XLJx7N6vF1/d2D9rFJ+X5Xp3rZDJk5kGvW7eu1PX6lixZ4l+eKiMjA5vNRo0aNSqyPCHEBTqzXahHVcnNd5GeZSPH6S4ezr0fDJitAcEP52ALmVe9b98+6tWrF7Bt4sSJpKenM2TIEPLy8hg2bBgTJkxgxowZcqWaECGusF2oqqpsST/JLwdO8Ht6FgfznL6pdLvWonPknL5CsGZD/33NOqgfE8Fbt3aotuEMQRziqFu3rn/8GeCbb74pdsyLL77o//rll1+ukLqEEGUj3mIm3mLm533HOJnvPr1DVU+1DFUpaNUVT90WqDUaBty3de1YkqIiKt0agmWt+v5qEkKUK7NBz9V14wLD2ePGuH8TnhoN0cKsuFK6FwtnHaAolXMNwbIm4wRCiHKTYS+ywLHHTcSP72A8vJP8q2/B1aIzlDB8EW7UM7hN/Sp32fbFkIAWQpQLp8fL1iNZvhv5NizL5mDIPooaZsVTu1mJ4Ryhgyd7pPB/3VMquNrQJAEthChzXlVl5oot/HHo5BmzNcy+DwRjaxe7T0KEkWHNYnlMwtlPAloIUeZe+2k7Czfsw56TEziVroRwVoAJXS7jH33asmPrlmo9a+NMEtBCiDLj9HjZl5nHvLW7OHwim/CVC4qE8zjUGg0CjjfqIDk+iml921X7DwRLIgEthLgkTo+XY3n5fLzxTxZu3Mfe47m4XadahibUR3Hk4Oj2t2LhrAPqxViIjTCRaXdSOzoiOC8ghElACyEuSmFHutVpGWxJz+JYXgFuVcXr9aKznfS3DLUNfCKgZagCRJkNtK4di0Gnw2o2VKllqsqSBLQQ4qIUdqQ7nO3gcLYDL4DHRcTKd/EmJeNu2BbVGhfQlQ6gfd1YrGbfijyqpsl857OQgBZCXDCnx8vKPUf580QeR3LzfQ33821ErHoPY8Ze9CcP4WreMSCcFSA+wkQNazg5BS7iIsxVcpmqsiQBLYS4IF5V5bkfNvPdziLLVOXbsH73OvqcY6jmCOw3PoQWZvXvVvAtVfVo15aM79KSTLuzyi5TVZYkoIUQF+S1n7bz0srU0xuKhrPRjL3XWNSYpID7hBl09GhWi8e6tUKv08kHgudJAloIcd5yC1zM/3m3b7wZiodzn3GoCfUD7hNu1DGpewpP9Ggtc5wvkAS0EOKcHC43M1ZsYdWeDPaftPs2+luG5pYazpfXjiXcqONv1zSVcL4IEtBCiFJ5VZXXftrOy6u2c9zuDFh925T2m69laEo3PHVbFgvnCKOO6DAjUWFGmUZ3kSSghRClmr1mB3P/u/OMrnQujAc246nREMPx/biSryo2lc6sU0iK9K1xJdPoLp4EtBCiRA6XmwXr9/BXTv7pjW4nlpXvYDiym/yrB+Jq0SWgK53FoCPCbMBiMtIiMYZuTWQa3aWQgBZClOjFH1PZdyzv9IaiXenCI/HUaQk6HTrAZNBRw2Jm8T1daV4zCpvTI9PoyoAEtBDCz+nxkml3YtLD/J934SrckW/D+u3r6HOPoRrDsPcehxqTiMWkkJIUh9mgJyrMSJvasae+NgXzZVQZEtBCiIC+GsdtBWxJzyLfc+ojwYBwPjVbI9Y3z9mkN/jPkmWsuexJQAshTvfVyHFwMMuOW9V8O9xOLCvfLhLOD6Em1PPfz+1RMel19Lmstow1lwMJaCGqOafHy+q0DNJzHKTnOALC2bT3V7w1GqLk23wtQ4uEsw5oUyeWxfd0kSGNciIBLUQ15vR4ST2aTUZeAVn5LjyF4ax60dmzfC1Do2pgGzAxoGUogKJAl+RECedyJAEtRDXkVTVeXZ3K6rQMjuUVsPdEHrYCFx4N8LiwLH8bT1Iy7kZXoFpii81zBoi3mJnco3XFF1+NSEALUQ19suskv+doHM5xcDQ3nwK391TL0DwsK9/FcHwfupwMXC06lRjODWIiePD65kSYjBVee3UiAS1ENeL0eEnPcfDbURsZLoX0HAdOj4oGkJ+H9ZvX0ecdRw2zYu8zLqBlKIBBgeSESO69tql8KFgBJKCFqAaKTqM7kuNg1wkHTi+nPxAsGs7GMOy9xqDGJPrvb1Kgc+MkOjROYGL3FDlzriAS0EJUA7PX7OCr1EPoFAWL2YjLq+Eu7Hx0ZjjfOA41vh46RQFNw2zQ0SIxmjdvv5ZGcZFBfR3VjQS0EFVc4TQ6naIAvrNpV2E4u52Ydq9DcdoCwhl8q6CgKCgoJEaGUztKmuxXNAloIaq49BwHR3IcWMxGNGDd/hO+HYUtQ1Uvzlbd8NRr6Q9n8E2jM+h06HUKV9aLk6sEgyBoAb1582ZmzZrFwoULSU1NZcyYMTRs2BCAYcOG0bdvX/+xqqoyZcoUdu3ahclkYtq0aTRo0CBIlQtRORSOO6/cc5S0TBsuj4rTc2q2htuJcf8fpbYMDTPoMOl1GPU6Eq1hTOyeErTXUZ0FJaAXLFjAV199RXi4r1/s9u3b+dvf/sbIkSNLPH7FihW4XC4+/fRTNm3axPPPP8/8+fMrsmQhKp2i4856nYLTq/qWqnIVYFm5AMPRveRfOQBXq64BLUMBmteMJsJoQK+DgSn15EPBIAnKGjT169dnzpw5/tvbtm1j9erV3HHHHUyePBmbzRZw/MaNG+nUqRMAbdu2Zdu2bRVarxCVTdFxZ1XT0ACDXvF9IPj1yxiO7kUNj8JTP6VYOINveCPeYmJgSj2ZThdEQTmD7t27N4cOHfLfbtOmDUOHDiUlJYX58+fzxhtv8Pjjj/v322w2rNbT8zH1ej0ejweD4ezlX2iQb9y48YKOrwihWBNIXRciGDUdd7g5cCwTk06HS9VwujzoHLlYv3kNfd4J3weCfcahRtcsdt9Yk44p7eNICDdi0hew6Y8/KrR2+R6eFhIfEvbs2ZOoqCj/11OnTg3Yb7Vasdvt/tuqqp4znAFSUlIwm89vLbSNGzfSvn37C6i6/IViTSB1XYhg1eT0eGmw20Ge08PBLDtOWw5h/ykSzjc+FDDPuVBcuJHHu7eif5fgXMJd3b6HTqfzrCeSIbHM7qhRo9iyZQsA69ato1WrVgH727Vrx5o1awDYtGkTzZo1q/AahahsrqgTx/7MXA6dOIl5+YLAcI6vG3CsQQddk2syuUdrJnRtVcojiooWEmfQU6ZMYerUqRiNRhISEvxn0BMnTmT8+PH07NmTtWvXcvvtt6NpGjNmzAhyxUKEJq+q8uLKbSzbmc6fmXmkn8zxtQyt2QjFafe1DD0VzjoFYsJMRIcbaRxnZcnfukpnuhATtICuW7cun332GQCtWrXik08+KXbMiy++6P/6ueeeq7DahKiMXB4P7V/5ht3Hc/GogNeDzlbYMjQB24CJKEWm0lmMelomRaNTFG5sUUfCOQSFxBm0EOLSDX7vJ3Ydy0XT8K2EsvytUy1D26NaT7cM1SmgauDVIMygl9VQQpgEtBBVwNFcO6v2ZuDV8LUMXbEAw4kD6PJO4GrZ1R/OGqBDwazXuLZ+DVkNJcRJQAtRiTlcbl78cRsLf/uTfI/3VOOjU7M1wiNPtQy1+I9XAINOIT5MT9+WtSWcQ5wEtBCVkC+YU1m86QCHcxzkezyB4WwMw95rbLF5zia9jsYJVnrUCpNhjUpAAlqISqSwv8a7v+71zW/2qugUBc1uCwznvg+jxtUBfGfNMeFG4iJMfDmyG40Toti2eRP6Eq4gFKFFAlqISmT2mh38e9tBMvIK0Ck6VFXF48rHuPtnFKejWDgbgCvqxmI2GhiYUo8WSbHBfQHigkhAC1FJFPbXcHtVnG4vAF7Ve7plaEo3PPVaocbVQQ9YzQZiI0wkRkXQNTlRhjQqIQloISqJY3kFbE7P4kiuwzfP2V2A8c/f8dRshOHYvoCWoTqdwv91b8Wd7ZOpGRkmvZwrKQloISoBp8fL27/s5kiOA48GuPKxrHgbQ8af5Lfvj6t1d1B8Y8o6BSJMem6/ohH1Yi1nf2AR0iSghQhhhR8Krth9lFV7j/rC2ZGD9bvZ6HNPoEbE4Gl4OSg6FPAva4WmnFqzSlRmEtBChLDZa3bwZeohtqVn4fSqvnD+9nX0eZmopnBfy9CoGoAvnAvzuVaUrCFYFcg8GyFCVOGHgoeyHWQXuAPD2RiO/caHUaNrFLmHBkCU2cDIa5vIuHMVIGfQQoQQp8dLpt2J1Wxge0Y2h7IdHM3NR3M7sax893Q4930YNa52wH0tJiO1o8IZeW0TxsuMjSpBAlqIEFA41rxq71G2Hskm0+HE7VXxejW8bqevZWhiIxR3Po6ufwsIZwVoHGfh63tvoGGcVc6cqxAJaCFCQOEFKAdO5nEop+DUYAXgcaOzn2oZGpmArf9jAatvA9SODqdmZDiRZqOEcxUjAS1EkGU5Cnjz590czsn3NTwq5CrAsvxNPImNcTe5GtUSWyycDTqFRnFWYsJNxFvOb3k3UXlIQAsRJIXDGm+t283ezMCV7HHk+uY5Zx5EZ8/ClXJDsXAGiAozoFMUuiYnytlzFSQBLUSQzF6zgy+2/sWhbEfgjsLVt22ZqOFR2G58KKBlKPimX0WFGbm2fg26N02Sy7irKAloIYKgcArdgSwb+R719I6i4WwKx9Z7LFpkAgBmg44wvY4WSdF8OqIzOkVHvMUsZ85VmAS0EEFwJCefNXuPkusqMuZ8Zjj3fQQttpZ/t1mvI85iZkibBtSNsQahalHRJKCFqGBeVeWmd1YGhrPbiWnvehR3QYnhbNJBnZgI/nZVsgxnVCMS0EJUsBd/3MaeY7mnN2iqr2Wo14OzVVfc9VsHhLMO+O6+HnRoVFOGM6oZCWghKlBegZOXV2/Hf+7sKsCU9huexGQMx/4MaBlaqGFshIRzNSUBLUQF8aoq7V/5lqx8t2+DMx/L8jcxHN9PQft+OFvf4G8ZWtSt7RpJOFdTEtBCVACvqtJ/wY+kFc53LvqBoDUOV6N2JYZzQoSJJ3u0ruBqRaiQgBaiAsz4YTNr/jzqu+HIxfrNq+htJ1HNFmy9H0SLjC92n5gwAxNvSCHCZKzgakWokIAWopw4PV7S85w89dZyVuw+igqB4WyK8F2EEpVQ7L41LGb+r3sr6UpXzUlAC1HGCi/hXp2WwU97jpDnPnUhituJZeU7p8O578MBszUAjDq4r0Mznu/XTs6chQS0EGVt9podfJV6CFXj9FWC/pahjVHcBdi7/a1YOIfpdXRrmsRLN18pHwoKIIgBvXnzZmbNmsXChQvZsWMHU6dORa/XYzKZeOGFF0hICPyzb+DAgURGRgJQt25dZs6cGYyyhTirwku4FUVh34lc3xqCHhc6Rw6K044aGY+t36NgCgu4X4RRxxV14rihaZKEs/ALSkAvWLCAr776ivDwcACmT5/O008/TYsWLfjkk09YsGABkyZN8h/vdDoBWLhwYTDKFeK8ZdqdZNqdHLMVcMxW4Ft9+4f5eGs2wtX8OtSI6IB5zpEmPc1qRlMrKpyuyYlylaAIEJSArl+/PnPmzGHixIkAvPLKK9SsWRMAr9eL2Rw4UX/nzp3k5+czcuRIPB4Pjz76KG3btq3osoU4p3iLGavZyPoDJ1AdOViWv4Xh5GF0+Xk42/QKCOdwg57Hu7Xi7muaStMjUaKgBHTv3r05dOiQ/3ZhOP/+++8sWrSIjz76KOD4sLAwRo0axdChQ9m/fz/33nsvy5Ytw2CQIXQROhwuN9OWb2b57iO+BV7/8xp6+0nUiBhsfcYFtAzVAd2aJjKxR2v0Olm7WZQsZBLu22+/Zf78+bz99tvExcUF7GvUqBENGjRAURQaNWpETEwMx48fp1atWqU8ms+2bdsuqIaNGzdecN3lLRRrAqmrKK+q8dGOEyzdc5J0hzcwnE0Rp1qGBs5zjg/TkWz2sOmPPyq83kLyPTx/waopJAL6yy+/5NNPP2XhwoXExMQU279kyRJ2797NlClTyMjIwGazUaNGjeIPdIaUlJRiwyWl2bhxI+3bt7/Q0stVKNYEUldRXlVl0D9XsWJPJgUeFezZWL95/XQ43/QIWkxSwH3CjTpa1E5gn8tIyuVtgzK0Id/D81eeNTmdzrOeSAb9byuv18v06dOx2+089NBD3HXXXcyePRuAiRMnkp6ezpAhQ8jLy2PYsGFMmDCBGTNmyPCGCAkvrtjK8l1HfOHsdvq60nmcpYazUQe1rOHoFIWTDt8HikKUJmgpV7duXT777DMAfv311xKPefHFF/1fv/zyyxVSlxDnw6uqzFixlZdXp+JUtcCWoSndfC1DzwhngHrRFurF+sai4yLMstCrOCs5DRXiAuUVOLnylW9PL/TqdGDa+yue2s0xZKSV2DIUoGGshfpxvpVQVE2ThV7FOUlAC3GeCi/hnrliC5n5Ht/GAruvZeiJvyhodxPONj1K7ErXu3ktDDqFrHwXcRFmmfMszosEtBDn6ZWftjPjh83kuk5dvm3Pxvrt677eGtZ4XMlXlhjOV9aN5Z3bOhJvMZNpd8qcZ3HeJKCFOA8Ol5up32/B7i4Szt+8it6e7WsZ2udBNGvg9FCjDhIjjCRFRfhDuXZ0RBCqF5WVBLQQpXB6vGTanVjNBsZ/8St296mFqoqGs8ni60p3xjxnvQL1YixE6VUZaxYXTQJaiDMUjjWv2pvBlvSTHMzJP73T7cTy47unz5z7PlxstoZBB1fUiSMxMpxks0fGmsVFu+CA/uGHH+jVq1d51CJESChsF3oo214snH0tQ5NRPG7s3e4pFs4NYiK4v2Mz7roymXiLmW2bN8ml3OKinfMnJy8vj2eeecZ/e/Hixdx///2kp6eXa2FCBENhu1CA9DPCObBl6IRi4dy8RiQPdbqMx7q1onZ0hAxriEt2zjPo4cOH89JLL/lvL1iwgG+//ZZ77rmHIUOGMHLkSLmqT1QZ6bkO0nPyOZbnoMB76gNBpwPL9/N8LUNbdCrWMhTgkU7Nmdb3ClkFRZSpc55B9+nThw8++CBgW9++fVm6dCnHjh1j0KBBbNiwodwKFKIieFWVV1enMvazX/j98EkO5Rb4dtiysHw7G0PmQQyHUtHMlmLhHBdmYEqfthLOosyd89T3wQcfJC0tLWDb7t27+eOPP7DZbGRkZHDfffdx0003MXnyZH8TfiEqk9fW7GDuT6n8lVukN4YtC+u3r6G3Z+O1xGDv81BAy9BCEWYDNqeHqDBTBVYsqoPz+vQiOTnZ//WVV17JI488wtatW7n22mtZsmQJGzZsoHHjxjz88MPlVqgQ5cXhcvPSiq3FwjnyVDirZgv2Xg+gWWOL3des15GSFCs9NUS5uKhZHGf2awb429/+xuLFi8ukKCEqisPlpvebyzme7z698VQ46/xT6R5Bi0ks8f61osLo0ayWfCAoysUFB3RJ4Vxo7ty5l1SMEBWlcK7zgnV72HUi7/QOtxPTnxvB6/GF803j0aJrlvgYegVSkmJknrMoN2U6/aJx48Zl+XBClJtXVqXy+da/2Fs0nP0tQ904W3XD3aBNqeEM0LpWDCgKHlVDL1OdRTmQ+XGiWvGqKq+s3s5Lq7ad7kgHKE47xt2/4KnXCsPRNF/joxJahhYy6hWsZpO/6b702BDlQQJaVCsvrtjKx5v2B4ZzgQ3LD/PRZx6iwOvBeXnPErvSASin/kWZjRh0ClazNN0X5UcCWlQLLo+Hwe//xIrdR3B5Nf92xZ6F9T+voXNk441MwNX0mhLDWTn1X4NeIcpspFVSjDTdF+VOAlpUC0M/WMOKXUdwqSWHs2q2Yu8zDs0SE3A/BejYMIHel9XmpMPFzmM55Ba4iQozStN9Ue4koEWVl1vg4sc9Zwtni2+B1zPmOZv1Cjc0rUX3pkk83LkFep3O34JUmu6LiiABLao0r6ry+NcbcBQ22gdwO4n48Z9FwjlwKp1Rp/D4DS0ZfU1zakaGBQSxNN0XFUkCWlRZXlVl8Hur+c/2w6c3FrYMTWqConqxd72n2FS6Xx65kbZ14xEi2CSgRZU1a1Uqy3cfwT+w4S5Ayc9DcTpQrXHY+j5SbCpdDYuZFkkxFV2qECWSgBZVUpajgBdWbqXA4xvaUJx2LMvm4anZAGerbmjhUcXCOcyg47GuLWRsWYQMuf5JVCleVeWFH7eS+PRicpy+NQQVWxaWb15Hf/IQhvTdYI4oFs5mHTzTqw0TurYKRtlClEjOoEWV8tpP23n6202cWt4VxZaF9ZtX0Tly8FrjfFPpzMVbhj7SpSWP39C6YosV4hzkDFpUGfluLy+s3EbhdShFw1kNs2Lv/UCxec4A8eFGnu7VpmKLFeI8SECLKsGrqvz9p7/IPNU29Mxwtt00Hi2qRrH7RZt0PH5DiqyGIkKSDHGISi+3wMXjX//GlmOnFnl1OzH+uRFUtdRwVoBmCVZGdWjGeLkaUIQoCWhRabk8Hoa8t5ofdh/FXXiVoKZhSttwqmVoV9wNLw8IZ70CNS1hfHpPZ66slyAzNkRIC9oQx+bNm7nrrrsAOHDgAMOGDWP48OE8++yzqKoacKyqqjzzzDPcdttt3HXXXRw4cCAYJYsQc+sHa/h25xF/OCsFNsxbV+Cp1QQtPApXi04B4WzSKXRoWJP29eMlnEWlEJSAXrBgAU899RROp28NuJkzZzJ+/Hg+/vhjNE1j5cqVAcevWLECl8vFp59+yt///neef/75YJQtQsjRXAfLd6X7L0JR8vOwfD+PsI3/wbjvD1zNOxabSlcnJgKdgnSgE5VGUAK6fv36zJkzx387NTWVq6++GoDOnTvz888/Bxy/ceNGOnXqBEDbtm3Ztm1bxRUrQkqWo4BHvlhPw6mfU3BquoZiO4n1q1noTx7GG10TV7MOoCgB97Oa9LRMjOHmVnWlA52oNIIyBt27d28OHTrkv61pGsqp/6EsFgt5eXkBx9tsNqxWq/+2Xq/H4/FgMJy9/AsN8o0bN17Q8RUhFGuCiq/L5VF5/L8H2ZBhx1lkBEyxnfR1pcvPQQ2PxN77QbSI6ID76oF3bmhA3SgTJn0Bm/74o0Jrl+/hhQnFuoJVU0h8SKjTnT6Rt9vtREVFBey3Wq3Y7Xb/bVVVzxnOACkpKZjN57faxcaNG2nfvv15VlwxQrEmqPi6vKpK21lfs+uYnSK99gPDOczqW327hH7OvS+rxdAe11VYvUXJ9/DChGJd5VmT0+k864lkSMyDbtmyJevXrwdgzZo1XHnllQH727Vrx5o1awDYtGkTzZo1q/AaRfA8v2IrOzNyA8IZt5OIVe+dDudS5jm3SIzi8791rahShShTIRHQjz/+OHPmzOG2227D7XbTu3dvACZOnEh6ejo9e/bEZDJx++23M3PmTCZNmhTkikVFcbjcvPrTTgLm9RRpGeqJr1dqOE/u0ZJNj/XHdB5/bQkRioL2k1u3bl0+++wzABo1asSiRYuKHfPiiy/6v37uuecqrDYRHCWtVjL529/JKnCdPsiVj1Jg87cMLbjxoRJX3+7VNImpN4bWn8pCXCg5tRBB51VVZq/Zweq0DH9Ad2yYwHvr09iTafMfpxTYsCybi7dGQwpa31Biy1CAy2pG8uXobhX5EoQoFxLQIuhmr9nBV6mH0CkKZoOePKeHZ5ZtxnPGbA3L9/PQ5x4HVYUr+5cYzj2a1uTb+3qi14XE6J0Ql0R+ikVQOT1eVqdloCsyb3nfidxi4Wz9z6voc4/jjUwosWVomF7Hfa3jJZxFlSI/ySKoMu1OMu2+K0o1TeNAlo2DOfn+/YXhrMvPRQ2LxN5rLFpE4DRMsw5aJkUzIDlOwllUKfLTLIIq3mIm3uIbqvgr205GXsHpy7fPCGffbI2EgPsrgCXMRGJkONFmuXxbVC0S0CKozAY9nRvXZF9mHvtO2nG4T62F4nZi3Oe74q+0cAYw6iAmzMgNTZMw6eXHWVQt8hMtgi7f7eFAtuP0Bk3D+OdGFI8LZ6uupYazDkhOiGLMdc2lv4aokmQWhwgar6ryyqpU/vH9Vv82JT8P0661uBtdAUfTcDduX3yBV71CnegIvhzVjeSEKOlMJ6osCWgRNLNWpfL6T9v9Vwkqjlws37+BPvsoaBrOtn0CutKZdNC2bjwmvY6BKfVomRQbnMKFqCAS0KLCeVWVGSu28vzKbRScmk+n5GVi/eY1dPm5eGOScF12XbGWoa1qxVLTGkbX5EQZ0hDVggS0qFAuj4d2L3/DzmO5p2dr5GX6FnjNz0MNj/Ktvh1+5lQ6hY/u7ETDOKsMaYhqQwJaVKhb3l3FjmO5/tuB4RyJ7aZHivVzBqgZFU6k2SjhLKoVmcUhKkxugYsfdh89vcHtJGL1+0XCeTxaZPHZGuEGHW1qxfrnSwtRXcgZtKgQXlXl8a83nm4beqplqCepKQCOrneXGM4RRj3t6sbRrYmsIyiqHwloUe68qsqAd39kReHZs9OBcuqfZo3F3mdcsal0ClA7Koy2deLp1kQ+FBTVkwS0KBeFvZ3DjTqGvvcTq/88hgYo+blYlr2Bt0YDCtr0QguPLD7PWacwqUcKo65tFtAbWojqRgJalKnC3s6r9maw7UgWh3MceE5N11DyTmD5fj76vBMAaOaIYuEcbtBxQ7NaTO7ZRhofiWpPAlqUqcLezoey7RzMdpy+CCXvxKl5znn+lqGYI/z3M+sVrm1Ygxsvq8OjXVtKOAuBBLQoQ06Pl5V7juL0eDmUbS8xnE/Pc4707QOuqh9H7+a1mdSjjQxnCFGEBLQoE15VZeaKraz9M4M8l7fIRSgnsP7nNXQFvnD2TaWL99/PqFPo3bw2T/e6XM6ahTiDBLQoE6+uTmXuf3eS6/Ke3uh2Yty/BRRdieEM0KFhDSb1kPFmIUoiAS0umdPjZdbqHWQVuE9v9LcMdeJs1QV3oyvQrHEB9zPrddzYoo4MawhRCjltEZds6+ETHD+1bBX4utKZ//gWT1IT1IhoXJddXyycAW5omsSjXVtWZKlCVCpyBi0uidPjpePsH/y3FXuOr2VoTgYoCs62NxbrSqcHel1Wm3+P6iZDG0KchQS0uCheVeWVn7bz/PLNFI46K7mnZmsU5OGNrYWrRedi4Rxh1DP5hhQm3pAi4SzEOUhAi4vyyupUnvp20+mLUIqEsxoRhb33g2hh1oD71I0K44HrL+PxG1oHoWIhKh8JaHHBXF6VF39MLTmcw6Ow3TTBP8+5UOPYCB64/jLpqSHEBZCAFhfE5fHwwIo/OZl/asaG20nETx+cDud+E4p9INgk3sov428kNiIsCBULUXlJQIvz5vJ4aPDcUo7ZXb4NhS1DazUFRcHR9Z5i4Vw/JpwxHZtJOAtxESSgxXnr/85Kjp2aTqcU2MFVgOLKR7PEYu/9YImNj8Z1aiHDGkJcpJAJ6KVLl/LFF18A4HQ62bFjB2vXriUqyrc23XvvvceSJUuIi/Odof3jH/+gcePGQau3OvGqKi+u2MqKPccAUBw5vpahCfUoaNsXLdxaLJzjwk3seKI/CdaIkh5SCHEeQiagBw0axKBBgwBf+A4ePNgfzgCpqam88MILpKSkBKvEamv2mh18tGkfAErucSw/zEeflwk6HZo5PCCcTTqFdvXiGNKmgYSzEJco5Caibt26lb1793LbbbcFbE9NTeXtt99m2LBhvPXWW0Gqrvo5Yctnxoqt7MzIQ8k9jvWb19DnZeKNqlmsZahJr5CcEMmQNg1kWEOIMqBomqad+7CKM27cOO68806uvfbagO1z585l+PDhWK1Wxo0bx7Bhw+jWrVuJj+F0Otm2bVtFlFtl5bu9vLP1GIt2ZvlWQjkVzroCG2p4NLZ+4wM+EKwZpuPmJnHc1SKecKP01hDiQqSkpGA2F18UOWSGOAByc3P5888/i4WzpmncfffdREb65tZ26dKF7du3lxrQhUp70SXZuHEj7du3v7jCy0kwavKqKq/9tJ2XVm3399c4Pc+55HDWAX88PpCkKEuF1nom+R6eP6nr/JVnTec6mQypIY7ffvuNjh07Fttus9no168fdrsdTdNYv369jEWXk9d+2s7MFdtONz9yOzEe2AI6PWpE8XDWK3Bji9pBD2chqqKQOoPet28fdevW9d/++uuvcTgc3HbbbUyYMIERI0ZgMpno0KEDXbp0CWKlVZPT42XB+j3kFrYNLdoytGXXUy1DY/3Hx5j1dEpO4rO7OwepYiGqtpAK6NGjRwfc7t+/v//rgQMHMnDgwAquqPrwqirTfthC2nEbKr6pdKbtP+Fq1gHDkT24G7cPmK0Radazc/It1LCGB69oIaq4kApoETyz1+xg+Z4jvg8E7VlYls1Dn3sMdAacVxRvGfr3rq0knIUoZxLQAqfHy+q0DMx6Hbrc40R887qvZWhcHVwtuxQL5zAd/F0a7QtR7iSgBZl2J5l2J87Mo0QUztaIiMHeayxa2OkP/xQgJsxAnQg92fluIkzG4BUtRDUgAV1NOT1eMu1O4i1m4i1mwh2Z7PrnFF84W2LI7+drGaoAJr0Os0FHSq0YTHo9nnw78Zbzm74ohLh4EtDVjFdVmb1mB6vTMvwB3S7RwoHFb+C156FYY9EN+DuWqDgKPF4UfIu7JljCMOn1qJpG+0SLLPQqRAWQgK5mZq/ZwVeph9ApCnqdwv92H2TF52vRRdZDn+jG3HsUEfE1KfB4iQk3EmE0gAIxYUasZgNdkxO5zpIf7JchRLUgAV2NOD1eVu45SoHHy5EcB0eOHsPrcWJy5eONiMHQbxwuo5m2NSNZfHdXbE6Pfyij8GzbbNCzcePGIL8SIaoHCehqwquqTF++mR93H8Gpaij2bCzfzcGbUJ+C9jehhUWCYiRcUdiSng1A7ejTjZCKfi2EqBghdam3KD+v/bSdef/b7QvnnGNYvnkNfd4J9DnH0EwRYDTjUVU0IN/lZe+JvGCXLES1J2fQVZzT42XfyTze/N8usgrcKDnHfI2PnHa8MUnY+zzgbxmqab5/4SY9TRIiz/HIQojyJgFdRXlVlVmrUnnv1zSO5jrIc3kDwlm1xGDvNQbNXGSeswKgcU39BKLCTEGrXQjhIwFdBXlVlcHvrWbF7iM4Paqvt0ZhP+dT4Wy7aTyaJTbgfia9ji7JidL8SIgQIQFdBb2yKpX/7TuG+9SYMm4nxr+2gcGIaig5nGPDjGx7vL+0DRUihEhAVzFOj5cVe47i8qh4Vd9iB8Z9v6O4C061DG1bLJwbxITz4PWXSTgLEWJkFkcVcyTHwfr9x7C7vWDLIuy3f+NJaoJqicHVvGOxcG6SYOGhTi0Y30WaHwkRauQMuoo4bsvnl/3HeWHlNvLcKkpeJpbv56HPO4FmMAe0DFXwrYTSOTmRL0d1k6ZHQoQoCehKqrDZUYRR4fo5P7D7eC7eU8v/KtkZWL993TeVLr4uzlZdA1qGRpkNXNe4Jv8e2Q29Tv6IEiJUSUBXMmc2O9qSnuUbzjilaDirlhhsvcb65zmDb0zr/o7NmNb3CglnIUKcBHQlU7TZEWilhrPXEoPtpgkQZg24f4RRz30dm0k4C1EJyP+llUjhyie6U8MVf2baTu90O4n470eB4WyJKfYYdWIs1I6SvhpCVAZyBl2JFK58YtTrcHq8ZNicvh1uJ6a9v+Kp1RRNb8DRZUSJ4Rxu0DHq2ibSy1mISkICuhKJCTeSXeDiaG4+tgI3AIojF7xuFFc+miUGR8/7A1bfLlTDYuaxri0Y37lFRZcthLhIEtCVyFs/7ybf5SWnwI2qgWI7iWXZXLzx9Si48ma0MGuJ4fzVyM70aF5XzpyFqGQkoCsJh8vNgnV7+CvL7uutkX0U6/fz0Tmy0cwRvqZHJYRzuEFHy6Q4CWchKiEJ6ErAq6r0W/Aju071aFayj2L9ZjY6lx1PbC3svR4AU1ix+5n1Cg3jrNJsX4hKSgI6xLk8Hi6f9R92Hy8ezl5LLPaeYwLmOQOYdGA1m4gON/K3q5Pl7FmISkoCOoR5VY0rZn1zOpxzjgWEs+2m8f7ZGgpwTf04osLMnHQUUMMazg1Nk3hYPhQUotKSgA5RTo+X2b8fZefxXN8GtxPjwVQwmvAaTQHhDBAbYWT52F7odbqABV6FEJWXBHSIKbyU+4ed6fywJ8u3MaBlaBdcja4oNs95/PWX+ZseyZizEFVDSAX0wIEDiYz0rYVXt25dZs6c6d/3448/8sYbb2AwGBg8eDC33nprsMosV6/9tJ25/93BXzkFgG8qnXnbKpytumA4sgd3o3YBszUUIDrMyIirmwSpYiFEeQmZgHY6fVfFLVy4sNg+t9vNzJkzWbJkCeHh4QwbNoxu3bpRo0aNii6zXGU78nny2z9wq77bSl4m1mVz0dlOopnCcF7Rt1hXugRLGC0So6kZGR6kqoUQ5SVkenHs3LmT/Px8Ro4cyYgRI9i0aZN/X1paGvXr1yc6OhqTyUT79u3ZsGFD8IotBw6XmybTvzwdztlHsX49C53tJJ6EBjhTugeEc7hBR4vEaOrGRNCtSaKMNwtRBYXMGXRYWBijRo1i6NCh7N+/n3vvvZdly5ZhMBiw2Wz+oQ8Ai8WCzWY7y6NVHoVjzvPX7iKr8PLtrCNYv52NzuXAa4nD3ut+MAWeIZsNeuIizDJTQ4gqLGQCulGjRjRo0ABFUWjUqBExMTEcP36cWrVqYbVasdvt/mPtdntAYJdm27ZtF1TDxo0bL7jui+XyquQ4vXydls1Ph3JIy3IBZ4SzNc43W8McuFZgUriefsnR3NMqCpO+gE1//FFhdReqyPfqQoRiXaFYE0hdFyJYNYVMQC9ZsoTdu3czZcoUMjIysNls/jHm5ORkDhw4QHZ2NhEREWzYsIFRo0ad8zFTUlIwm4tf/lySjRs30r59+0t6Deej8Ix51d6jbDmSTUZePq7CpVDcTiL+96/AcI6IDrh/vagwHurSkvGdWwStp3NFvVcXKhTrCsWaQOq6EOVZk9PpPOuJZMgE9JAhQ5g0aRLDhg1DURRmzJjBd999h8Ph4LbbbuOJJ55g1KhRaJrG4MGDSUxMDHbJF6Ww4f7hbAdZDldAOJv2/oqndjM0gwlHl7uKhTPA1/feQOvacRVctRAiGEImoE0mEy+//HLAtnbt2vm/7t69O927d6/osspUYcN9DTiSl0+Bx7caiuLIAa8HxV2AFhGNo8e9JbcMjTDSrGbx0BZCVE0hE9DVQWHD/fRcB/luLxqnp9J54+qSf/XAUluGxoYZ+Xv3FJmtIUQ1IgFdgeItZqLDjWw6XOAL56wjWH+Yj86RgxoehRZWvGVomEGhfoyV+zo0ldkaQlQzEtAVyGzQ41FVnB41YLaGJ7YO9l5jwRjYMjTGqPBQ11ZM7J7iv4xbCFF9SEBXIKfHi6IpmHKPYiwylc7ea0xAP2ejDjo3TuTJttF06XBFECsWQgSTBHQFcHq8HMy2M3fNDn7dkorp69dQXPm+cL5pAnpLFKoGNSJMDG7bgBk3tSMqzBSS80GFEBVHAroceVWV19bs4P31e/kz04bbmY/hzy2YzRGopnDsN02AiChQIMKgZ3yXFjzWPSVo85uFEKFFAroczV6zgzfX7uKE3YlH9WLc9we4Ciho0Qldk6uIio5FAZITIhl6eQP+r3tKsEsWQoQQOVUrJ06Pl5V7jpJT4IbcE5h+Xoy3VlM0ayye5h3xhkeiaRqKotC3RW0e7doy2CULIUKMnEGXk0y7k2N5BbhPZqB8/TpGWxZamBV3u74oKJgNeprWiKKmNYxJPdrIsIYQohgJ6HJiNRsw5h5BWfoiOB2oNRvhSumOoigogMmgI9yo54amSXLxiRCiRBLQZaywGdJ//vcbO96a4gvnyHiUm8Zi1IfjVlX0OoUkaxgDU+rJxSdCiFJJQJex2Wt28OmPP7P3nefA6UAXnYDrpglgCCfcqKdBpIUhbRrwxA1y8YkQ4uwkoMuQ0+Nl5Y4DHPryHdQCO6bYmjS77zn01mj0isLcIVfTKC5ShjSEEOdFAroMHTyexZ9rlxPZuBU6k5mGQ8ZhjIwBwOn1EmU2STgLIc6bBHQZOXToELjchGkePJGxNL7jMfRFLt+OizATbzm/xQOEEAIkoMvEvn37uPnmm2ndujX9B45k1WF7QDirmkbXZFnYVQhxYWTy7SX6Y/MWevXpw+HDh8nKyuLRPldzyxVNsJoNuLxerGYDN7eqK7M1hBAXTM6gL5JXVXnyw695d/I4vAV2LHUa0eXh57BarUzo2ooHrr+MTLuTeItZzpyFEBdFzqAvgNPjJT3HgdPj5ckPv+adyQ/iLbBjiqtJozsf5/s/TzJ7zQ7A1/u5dnSEhLMQ4qLJGfR5KLz4ZHVaBpl2J6bcY2x+YzKaMx9TXE2a3fucf7bG6rQMHrj+MglmIcQlkzPo81C4ErfN6cGgujmSuhHVZEEXXSMgnAFOOnzrDgohxKWSM+hzKFyJW6coaJpG9tZ14MpH16YrWtMr0VsDV9mW6XRCiLIiZ9Bn4fR4ST2azXGbk4ITRzj477exNk7BHJNAXPvueMMicXtV//EynU4IUZbkDLoEXlXllVWprNhzlKx8J/v2pqF9PRvVloXBGkOtHrcSpyiE5eUTE24ip8BFXISZrsmJMp1OCFFmJKDP4FVVBr67irX7juFwedAyDxH+7Rx07gKo1YSanQegKAqqpjHymiYynU4IUW4koIvwqir931nJ97uOAqDLPITlO184eyMTKLhhNAdtblpHWf1ny3qdjtrREUGuXAhRFUlAn+JVNQa882Op4Wy/aTwWi5X6sVYW3nE9UWGmIFcshKjq5EPCUxbtyOTHvb5wxu0kfN1n/nC23TQeLTwSg6LgVlVsTk9wixVCVAtyBo1vtsYvR/JwejVwOzHt/RVPrWZoxnAcne+E8EgAosKNJFhkGp0QomJIQONb4DXH5UHJy/RtcDvRIqJxdO8JRl8Y6xWoF2uVaXRCiAoTEgHtdruZPHkyhw8fxuVyMXbsWG644Qb//vfee48lS5YQFxcHwD/+8Q8aN25cZs8fbzETbsvE+u3reGNrk99hKFqY1R/OAA1jLdwiawgKISpQSAT0V199RUxMDC+99BJZWVnccsstAQGdmprKCy+8QEpKSrk8/+4d28n410voHLmoUTWKhXN8hJH1E/oSGxF2lkcRQoiyFRIB3adPH3r37u2/rdcHDiGkpqby9ttvc/z4cbp27cr9999fZs+9detW+vfvj9OWi7VuMid7jEGvM4IGRoOOpMhwHriumYSzEKLCKZqmacEuopDNZmPs2LHceuut9O/f37997ty5DB8+HKvVyrhx4xg2bBjdunUr9XGcTifbtm075/OlpaXx97//HbvdTp06dXj5lVf45oiXX47YyHOrxIcZuCrJyu3N49DrlDJ5jUIIcaaUlBTM5hImH2ghIj09Xbvlllu0xYsXB2xXVVXLzc313160aJE2d+7csz5WQUGBtmHDBq2goKDUY3bu3Kk1aNBAi42N1dq3b68tX7789P3dHu1wtl0rcHsu8tWUjQ0bNgT1+UsjdZ2/UKxJ06SuC1GeNZ0rq0JiHvSJEycYOXIk//d//8eQIUMC9tlsNvr164fdbkfTNNavX3/JY9F2u51Vq1aRkJBA48aN+e6774iNjfXvl2b7QohQEBJj0G+++Sa5ubnMmzePefPmATB06FDy8/O57bbbmDBhAiNGjMBkMtGhQwe6dOly0c+laRr//ve/yc7OZvTo0QwePJgaNWrw119/ldXLEUKIMhESAf3UU0/x1FNPlbp/4MCBDBw48JKfZ8+ePbz66qs88cQT/Pe//2XgwIFYLJZLflwhhCgPIRHQFWHnzp3ccsstZGRkUKdOHSZPnoyiyAd/QojQVS0CevPmzdx8883k5eXRqVMnxo8fL+EshAh5IfEhYXkqGs7JycksXLhQhjWEEJVClQ7oHTt2BITzd999R1RUVLDLEkKI81KlA3rmzJnk5eXRpEkTvvvuOxISEoJdkhBCnLcqPQZ9zTXXAPDGG29IOAshKp0qGdDaqavXa9WqxZtvvklERAROp/Oc9zufYypaKNYEUteFCMWaQOq6EOVVk8vlAk5n1plCqhdHWcnLy2P37t3BLkMIIc5Ls2bNiIyMLLa9Sga0qqrY7XaMRqNMpxNChCxN03C73VgsFnS64h8JVsmAFkKIqqBKz+IQQojKTAJaCCFClAS0EEKEKAloIYQIUVVyHnRpgr16+NkMHDjQP82mbt26zJw507/vxx9/5I033sBgMDB48GBuvfXWCqlp6dKlfPHFF4BvHuiOHTtYu3at/3L5in6/Nm/ezKxZs1i4cCEHDhzgiSeeQFEUmjZtyrPPPhvwKbiqqkyZMoVdu3ZhMpmYNm0aDRo0KPe6duzYwdSpU9Hr9ZhMJl544YViF0md7XtdHjWlpqYyZswYGjZsCMCwYcPo27ev/9hgvVcTJkzgxIkTABw+fJjLL7+cV199NeD48n6vSsqEJk2ahMzPVsgseVURlixZok2bNk3TNE07efKk1qVLl4D9f//737WtW7dWeF0FBQXagAEDStzncrm0Hj16aNnZ2ZrT6dQGDRqkHTt2rGIL1DRtypQp2ieffBKwrSLfr7ffflvr16+fNnToUE3TNO3+++/XfvnlF03TNO3pp5/Wfvjhh4Djv//+e+3xxx/XNE3T/vjjD23MmDEVUtcdd9yhbd++XdM0TfvXv/6lzZgxI+D4s32vy6umzz77THv33XdLPT5Y71Wh7Oxs7eabb9YyMjICtlfEe1VSJoTKz5amhciSVxWlT58+PPLII/7bpa0ePmzYMN56660Kq2vnzp3k5+czcuRIRowYwaZNm/z70tLSqF+/PtHR0ZhMJtq3b8+GDRsqrDbwrXy+d+9ebrvttoDtFfl+1a9fnzlz5gQ899VXXw1A586d+fnnnwOO37hxI506dQKgbdu257WIcFnU9corr9CiRQsAvF5vsYVAz/a9Lq+atm3bxurVq7njjjuYPHkyNpst4PhgvVeF5syZw5133knNmjUDtlfEe1VSJoTKzxZUszFoi8WC1WrFZrPx8MMPM378+ID9N910E1OmTOGDDz5g48aNrFq1qkLqCgsLY9SoUbz77rv84x//4LHHHsPj8QC+NRmLXmFksViK/Q9W3t566y0efPDBYtsr8v3q3bs3BsPpETlN0/wXIVksFvLy8gKOt9lsWK1W/229Xu9/T8uzrsKQ+f3331m0aBH33HNPwPFn+16XV01t2rRh4sSJfPTRR9SrV4833ngj4PhgvVcAmZmZrFu3jkGDBhU7viLeq5IyIVR+tqCaBTTAkSNHGDFiBAMGDKB///7+7ZqmcffddxMXF4fJZKJLly5s3769Qmpq1KgRN998M4qi0KhRI2JiYjh+/DgAVqsVu93uP9Zut5d4SWh5yc3N5c8//+Taa68N2B7M9wsIGBO02+3F2sie+b6pqlosHMrLt99+y7PPPsvbb7/tH58vdLbvdXnp2bOnf6Hlnj17Fvs+BfO9WrZsGf369Sv21yxU3Ht1ZiaE0s9WtQroil49/HwtWbKE559/HoCMjAxsNhs1atQAIDk5mQMHDpCdnY3L5WLDhg1cccUVFVIXwG+//UbHjh2LbQ/m+wXQsmVL1q9fD8CaNWu48sorA/a3a9eONWvWALBp0yaaNWtWIXV9+eWXLFq0iIULF1KvXr1i+8/2vS4vo0aNYsuWLQCsW7eOVq1aBewP1ntVWE/nzp1L3FcR71VJmRBKP1vVahZHRa4efiGGDBnCpEmTGDZsGIqiMGPGDL777jscDge33XYbTzzxBKNGjULTNAYPHkxiYmKF1AWwb98+6tat67/99ddf++sK1vsF8Pjjj/P000/zyiuv0LhxY3r37g3AxIkTGT9+PD179mTt2rXcfvvtaJrGjBkzyr0mr9fL9OnTqVWrFg899BAAV111FQ8//LC/rpK+1+V9tjplyhSmTp2K0WgkISGBqVOnAsF9rwrt27ev2C+yinyvSsqEJ598kmnTpoXEz5b04hBCiBBVrYY4hBCiMpGAFkKIECUBLYQQIUoCWgghQpQEtBBChCgJaCGECFES0EIIEaIkoIU4i8zMTNq3b4+qqv5to0ePZtmyZUGsSlQXEtBCnEV8fDwJCQns3r0b8PXZUBSFPn36BLkyUR1Uq0u9hbgYV155JX/88Qd169bl1Vdf5Z///GewSxLVhAS0EOdw5ZVX8ssvv7B3714GDx5cYhMkIcqD9OIQ4hwOHjzIkCFDqFmzJp9//jkmkynYJYlqQsaghTiH2rVr43K5ePrppyWcRYWSgBbiHD788EP69u3rXwZJiIoiY9BClCItLY1x48ZRu3ZtZs+eHexyRDUkY9BCCBGiZIhDCCFClAS0EEKEKAloIYQIURLQQggRoiSghRAiRElACyFEiJKAFkKIECUBLYQQIer/ASTFA6w2fisnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando gráfico de erro\n",
    "plot_model(tune_br, plot='error')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f03540b",
   "metadata": {},
   "source": [
    "Podemos observar no gráfico acima o ajuste do nosso modelo diante do conjunto de dados, indicando o R² de 0,999, mostrando que 99,9% dos dados estão bem ajustados ao modelo **Bayesian Ridge** após o *tunning*, o que é um sinal positivo para a funcionalidade do modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "20ff772b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tune_br, plot='feature') # feature melhor utilizada no modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26088d37",
   "metadata": {},
   "source": [
    "No gráfico de *Feature Importance Plot*, podemos observar as variáveis organizadas pelo seu nível de importância para o modelo de previsão. Neste caso, observamos que o preço mínimo do dia tem sido a variável mais utilizada, enquanto o volume e a média móvel de 30 períodos não foram utilizadas pelo modelo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "634c4dcd",
   "metadata": {},
   "source": [
    "## Testando o modelo com dados de treinamento"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4404742f",
   "metadata": {},
   "source": [
    "Iremos realizar um teste no modelo utilizando os dados de treinamento, para observar como as métricas de acurácia se comparam com os resultados atingidos anteriormente quando estávamos realizando o *tuning* do modelo Bayesian Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "39cb1454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_4f839\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4f839_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_4f839_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_4f839_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_4f839_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_4f839_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_4f839_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_4f839_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4f839_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_4f839_row0_col0\" class=\"data row0 col0\" >Bayesian Ridge</td>\n",
       "      <td id=\"T_4f839_row0_col1\" class=\"data row0 col1\" >0.0849</td>\n",
       "      <td id=\"T_4f839_row0_col2\" class=\"data row0 col2\" >0.0125</td>\n",
       "      <td id=\"T_4f839_row0_col3\" class=\"data row0 col3\" >0.1118</td>\n",
       "      <td id=\"T_4f839_row0_col4\" class=\"data row0 col4\" >0.9993</td>\n",
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       "<pandas.io.formats.style.Styler at 0x1f4247c5f40>"
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Volume</th>\n",
       "      <th>SMA7</th>\n",
       "      <th>SMA30</th>\n",
       "      <th>Close</th>\n",
       "      <th>Label</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.861416</td>\n",
       "      <td>4.861416</td>\n",
       "      <td>4.669681</td>\n",
       "      <td>44085700.0</td>\n",
       "      <td>4.880000</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>4.700606</td>\n",
       "      <td>4.704719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.697330</td>\n",
       "      <td>7.755335</td>\n",
       "      <td>7.482709</td>\n",
       "      <td>39540700.0</td>\n",
       "      <td>7.820000</td>\n",
       "      <td>8.270000</td>\n",
       "      <td>7.546515</td>\n",
       "      <td>7.568242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.591805</td>\n",
       "      <td>10.644011</td>\n",
       "      <td>10.446792</td>\n",
       "      <td>22821900.0</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>10.590000</td>\n",
       "      <td>10.527999</td>\n",
       "      <td>10.515177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.077955</td>\n",
       "      <td>17.168692</td>\n",
       "      <td>16.967775</td>\n",
       "      <td>38201600.0</td>\n",
       "      <td>17.410000</td>\n",
       "      <td>17.799999</td>\n",
       "      <td>17.006662</td>\n",
       "      <td>17.061857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.925508</td>\n",
       "      <td>2.950248</td>\n",
       "      <td>2.721403</td>\n",
       "      <td>100576600.0</td>\n",
       "      <td>2.970000</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>2.727588</td>\n",
       "      <td>2.777303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>2.783253</td>\n",
       "      <td>2.826548</td>\n",
       "      <td>2.721403</td>\n",
       "      <td>56031000.0</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>3.010000</td>\n",
       "      <td>2.758513</td>\n",
       "      <td>2.766190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>11.577898</td>\n",
       "      <td>11.925932</td>\n",
       "      <td>11.514093</td>\n",
       "      <td>41785200.0</td>\n",
       "      <td>11.180000</td>\n",
       "      <td>10.840000</td>\n",
       "      <td>11.769318</td>\n",
       "      <td>11.794829</td>\n",
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       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>5.028411</td>\n",
       "      <td>5.071706</td>\n",
       "      <td>4.576906</td>\n",
       "      <td>86084704.0</td>\n",
       "      <td>4.690000</td>\n",
       "      <td>3.380000</td>\n",
       "      <td>4.576906</td>\n",
       "      <td>4.690520</td>\n",
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       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>20.407734</td>\n",
       "      <td>20.707748</td>\n",
       "      <td>20.267727</td>\n",
       "      <td>48744700.0</td>\n",
       "      <td>20.040001</td>\n",
       "      <td>18.520000</td>\n",
       "      <td>20.574409</td>\n",
       "      <td>20.525423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>8.250798</td>\n",
       "      <td>8.300279</td>\n",
       "      <td>8.139468</td>\n",
       "      <td>44651900.0</td>\n",
       "      <td>8.350000</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>8.139468</td>\n",
       "      <td>8.199902</td>\n",
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       "  </tbody>\n",
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       "<p>660 rows × 8 columns</p>\n",
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      ],
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       "          Open       High        Low       Volume       SMA7      SMA30  \\\n",
       "0     4.861416   4.861416   4.669681   44085700.0   4.880000   4.750000   \n",
       "1     7.697330   7.755335   7.482709   39540700.0   7.820000   8.270000   \n",
       "2    10.591805  10.644011  10.446792   22821900.0  10.700000  10.590000   \n",
       "3    17.077955  17.168692  16.967775   38201600.0  17.410000  17.799999   \n",
       "4     2.925508   2.950248   2.721403  100576600.0   2.970000   3.900000   \n",
       "..         ...        ...        ...          ...        ...        ...   \n",
       "655   2.783253   2.826548   2.721403   56031000.0   2.780000   3.010000   \n",
       "656  11.577898  11.925932  11.514093   41785200.0  11.180000  10.840000   \n",
       "657   5.028411   5.071706   4.576906   86084704.0   4.690000   3.380000   \n",
       "658  20.407734  20.707748  20.267727   48744700.0  20.040001  18.520000   \n",
       "659   8.250798   8.300279   8.139468   44651900.0   8.350000   7.930000   \n",
       "\n",
       "         Close      Label  \n",
       "0     4.700606   4.704719  \n",
       "1     7.546515   7.568242  \n",
       "2    10.527999  10.515177  \n",
       "3    17.006662  17.061857  \n",
       "4     2.727588   2.777303  \n",
       "..         ...        ...  \n",
       "655   2.758513   2.766190  \n",
       "656  11.769318  11.794829  \n",
       "657   4.576906   4.690520  \n",
       "658  20.574409  20.525423  \n",
       "659   8.139468   8.199902  \n",
       "\n",
       "[660 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testando com dados de treinamento para ver as métricas de acurácia\n",
    "predict_model(tune_br)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53fa8869",
   "metadata": {},
   "source": [
    "Métricas ainda melhores!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3149be63",
   "metadata": {},
   "source": [
    "## Finalizando o modelo e fazendo a previsão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1e2f6070",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finalizando o modelo e fazendo a previsão dos últimos 253 dias de pregão\n",
    "final_br_model = finalize_model(tune_br)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5fc62d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_013d3_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_013d3_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_013d3_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_013d3_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_013d3_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_013d3_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_013d3_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_013d3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_013d3_row0_col0\" class=\"data row0 col0\" >Bayesian Ridge</td>\n",
       "      <td id=\"T_013d3_row0_col1\" class=\"data row0 col1\" >0.1521</td>\n",
       "      <td id=\"T_013d3_row0_col2\" class=\"data row0 col2\" >0.0368</td>\n",
       "      <td id=\"T_013d3_row0_col3\" class=\"data row0 col3\" >0.1919</td>\n",
       "      <td id=\"T_013d3_row0_col4\" class=\"data row0 col4\" >0.9976</td>\n",
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       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.81</td>\n",
       "      <td>18.82</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.21</td>\n",
       "      <td>19.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.64</td>\n",
       "      <td>19.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.50</td>\n",
       "      <td>19.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19.75</td>\n",
       "      <td>19.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>30.06</td>\n",
       "      <td>30.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>30.02</td>\n",
       "      <td>30.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>29.76</td>\n",
       "      <td>29.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>30.28</td>\n",
       "      <td>30.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>30.30</td>\n",
       "      <td>30.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Close  Label\n",
       "0    18.81  18.82\n",
       "1    19.21  19.18\n",
       "2    19.64  19.55\n",
       "3    19.50  19.38\n",
       "4    19.75  19.64\n",
       "..     ...    ...\n",
       "248  30.06  30.11\n",
       "249  30.02  30.22\n",
       "250  29.76  29.71\n",
       "251  30.28  30.16\n",
       "252  30.30  30.04\n",
       "\n",
       "[253 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prevendo os ultimos 253 dias\n",
    "prev = predict_model(final_br_model, data = petr4_predict)\n",
    "prev[['Close','Label']].round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada37c95",
   "metadata": {},
   "source": [
    "## Plotando gráfico de preços de fechamento e de previsão dos últimos 253 dias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e61df2aa",
   "metadata": {},
   "outputs": [
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stroke-opacity: 1; fill: rgb(255, 255, 255); fill-opacity: 1; stroke-width: 0px;\"/><g class=\"scrollbox\" transform=\"\" clip-path=\"url(#legendbc306c)\"><text class=\"legendtitletext\" text-anchor=\"start\" x=\"2\" y=\"18.2\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">variable</text><g class=\"groups\" transform=\"\"><g class=\"traces\" transform=\"translate(0,32.7)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(42, 63, 95); fill-opacity: 1; white-space: pre;\">Preço fechamento</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(99, 110, 250); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"/></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"148.109375\" height=\"19\" style=\"fill: rgb(0, 0, 0); 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font-size: 17px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">Preço fechamento x preço previsto de PETR4 entre 01/06/2021 e 06/06/2021</text></g><g class=\"g-xtitle\"><text class=\"xtitle\" x=\"694.5\" y=\"960.8\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">index</text></g><g class=\"g-ytitle\"><text class=\"ytitle\" transform=\"rotate(-90,38.934375,510)\" x=\"38.934375\" y=\"510\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(42, 63, 95); opacity: 1; font-weight: normal; white-space: pre;\">value</text></g></g></svg>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.line(round(prev[['Close','Label']], 2), \n",
    "              x = round(prev[['Close','Label']], 2).index, \n",
    "              y = ['Close','Label'],\n",
    "             title = 'Preço fechamento x preço previsto de PETR4 entre 01/06/2021 e 06/06/2021',\n",
    "             width = 1500, height = 1000)\n",
    "newnames = {'Close':'Preço fechamento', 'Label': 'Preço previsto'}\n",
    "fig.for_each_trace(lambda t: t.update(name = newnames[t.name],\n",
    "                                      legendgroup = newnames[t.name],\n",
    "                                      hovertemplate = t.hovertemplate.replace(t.name, newnames[t.name])\n",
    "                                     )\n",
    "                  )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc6476ac",
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   "source": [
    "## Conclusão<br>\n",
    "Podemos observar que a biblioteca PyCaret oferece formas fáceis e simples, com pouquíssimas linhas de código, para testarmos diversos modelos diferentes de machine learning e vermos qual se adéqua melhor ao conjunto dos dados analisados.<br>\n",
    "Neste estudo, foi possível atingir um modelo que se ajustou muito bem aos nossos dados e que foi capaz de prever os preços de fechamento dos últimos 253 dias de pregão de forma bastante satisfatória, sendo capaz de indicar a direção das ações da Petrobrás naquele período.<br>\n",
    "Todos os dados e os procedimentos presentes neste *notebook* tem como única finalidade o estudo da biblioteca PyCaret e os modelos de machine learning utilizados, não sendo, de maneira nenhuma, indicação de compra ou venda de quaisquer ativos financeiros."
   ]
  }
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